research papers on sports

• Systematic review update
• Open access
• Published: 21 June 2023

The impact of sports participation on mental health and social outcomes in adults: a systematic review and the ‘Mental Health through Sport’ conceptual model

• Narelle Eather   ORCID: orcid.org/0000-0002-6320-4540 1 , 2 ,
• Levi Wade   ORCID: orcid.org/0000-0002-4007-5336 1 , 3 ,
• Aurélie Pankowiak   ORCID: orcid.org/0000-0003-0178-513X 4 &
• Rochelle Eime   ORCID: orcid.org/0000-0002-8614-2813 4 , 5  

Systematic Reviews volume  12 , Article number:  102 ( 2023 ) Cite this article

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Sport is a subset of physical activity that can be particularly beneficial for short-and-long-term physical and mental health, and social outcomes in adults. This study presents the results of an updated systematic review of the mental health and social outcomes of community and elite-level sport participation for adults. The findings have informed the development of the ‘Mental Health through Sport’ conceptual model for adults.

Nine electronic databases were searched, with studies published between 2012 and March 2020 screened for inclusion. Eligible qualitative and quantitative studies reported on the relationship between sport participation and mental health and/or social outcomes in adult populations. Risk of bias (ROB) was determined using the Quality Assessment Tool (quantitative studies) or Critical Appraisal Skills Programme (qualitative studies).

The search strategy located 8528 articles, of which, 29 involving adults 18–84 years were included for analysis. Data was extracted for demographics, methodology, and study outcomes, and results presented according to study design. The evidence indicates that participation in sport (community and elite) is related to better mental health, including improved psychological well-being (for example, higher self-esteem and life satisfaction) and lower psychological ill-being (for example, reduced levels of depression, anxiety, and stress), and improved social outcomes (for example, improved self-control, pro-social behavior, interpersonal communication, and fostering a sense of belonging). Overall, adults participating in team sport had more favorable health outcomes than those participating in individual sport, and those participating in sports more often generally report the greatest benefits; however, some evidence suggests that adults in elite sport may experience higher levels of psychological distress. Low ROB was observed for qualitative studies, but quantitative studies demonstrated inconsistencies in methodological quality.

Conclusions

The findings of this review confirm that participation in sport of any form (team or individual) is beneficial for improving mental health and social outcomes amongst adults. Team sports, however, may provide more potent and additional benefits for mental and social outcomes across adulthood. This review also provides preliminary evidence for the Mental Health through Sport model, though further experimental and longitudinal evidence is needed to establish the mechanisms responsible for sports effect on mental health and moderators of intervention effects. Additional qualitative work is also required to gain a better understanding of the relationship between specific elements of the sporting environment and mental health and social outcomes in adult participants.

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Introduction

The organizational structure of sport and the performance demands characteristic of sport training and competition provide a unique opportunity for participants to engage in health-enhancing physical activity of varied intensity, duration, and mode; and the opportunity to do so with other people as part of a team and/or club. Participation in individual and team sports have shown to be beneficial to physical, social, psychological, and cognitive health outcomes [ 1 , 2 , 3 , 4 , 5 , 6 , 7 ]. Often, the social and mental health benefits facilitated through participation in sport exceed those achieved through participation in other leisure-time or recreational activities [ 8 , 9 , 10 ]. Notably, these benefits are observed across different sports and sub-populations (including youth, adults, older adults, males, and females) [ 11 ]. However, the evidence regarding sports participation at the elite level is limited, with available research indicating that elite athletes may be more susceptible to mental health problems, potentially due to the intense mental and physical demands placed on elite athletes [ 12 ].

Participation in sport varies across the lifespan, with children representing the largest cohort to engage in organized community sport [ 13 ]. Across adolescence and into young adulthood, dropout from organized sport is common, and especially for females [ 14 , 15 , 16 ], and adults are shifting from organized sports towards leisure and fitness activities, where individual activities (including swimming, walking, and cycling) are the most popular [ 13 , 17 , 18 , 19 ]. Despite the general decline in sport participation with age [ 13 ], the most recent (pre-COVID) global data highlights that a range of organized team sports (such as, basketball, netball volleyball, and tennis) continue to rank highly amongst adult sport participants, with soccer remaining a popular choice across all regions of the world [ 13 ]. It is encouraging many adults continue to participate in sport and physical activities throughout their lives; however, high rates of dropout in youth sport and non-participation amongst adults means that many individuals may be missing the opportunity to reap the potential health benefits associated with participation in sport.

According to the World Health Organization, mental health refers to a state of well-being and effective functioning in which an individual realizes his or her own abilities, is resilient to the stresses of life, and is able to make a positive contribution to his or her community [ 20 ]. Mental health covers three main components, including psychological, emotional and social health [ 21 ]. Further, psychological health has two distinct indicators, psychological well-being (e.g., self-esteem and quality of life) and psychological ill-being (e.g., pre-clinical psychological states such as psychological difficulties and high levels of stress) [ 22 ]. Emotional well-being describes how an individual feels about themselves (including life satisfaction, interest in life, loneliness, and happiness); and social well–being includes an individual’s contribution to, and integration in society [ 23 ].

Mental illnesses are common among adults and incidence rates have remained consistently high over the past 25 years (~ 10% of people affected globally) [ 24 ]. Recent statistics released by the World Health Organization indicate that depression and anxiety are the most common mental disorders, affecting an estimated 264 million people, ranking as one of the main causes of disability worldwide [ 25 , 26 ]. Specific elements of social health, including high levels of isolation and loneliness among adults, are now also considered a serious public health concern due to the strong connections with ill-health [ 27 ]. Participation in sport has shown to positively impact mental and social health status, with a previous systematic review by Eime et al. (2013) indicated that sports participation was associated with lower levels of perceived stress, and improved vitality, social functioning, mental health, and life satisfaction [ 1 ]. Based on their findings, the authors developed a conceptual model (health through sport) depicting the relationship between determinants of adult sports participation and physical, psychological, and social health benefits of participation. In support of Eime’s review findings, Malm and colleagues (2019) recently described how sport aids in preventing or alleviating mental illness, including depressive symptoms and anxiety or stress-related disease [ 7 ]. Andersen (2019) also highlighted that team sports participation is associated with decreased rates of depression and anxiety [ 11 ]. In general, these reviews report stronger effects for sports participation compared to other types of physical activity, and a dose–response relationship between sports participation and mental health outcomes (i.e., higher volume and/or intensity of participation being associated with greater health benefits) when adults participate in sports they enjoy and choose [ 1 , 7 ]. Sport is typically more social than other forms of physical activity, including enhanced social connectedness, social support, peer bonding, and club support, which may provide some explanation as to why sport appears to be especially beneficial to mental and social health [ 28 ].

Thoits (2011) proposed several potential mechanisms through which social relationships and social support improve physical and psychological well-being [ 29 ]; however, these mechanisms have yet to be explored in the context of sports participation at any level in adults. The identification of the mechanisms responsible for such effects may direct future research in this area and help inform future policy and practice in the delivery of sport to enhance mental health and social outcomes amongst adult participants. Therefore, the primary objective of this review was to examine and synthesize all research findings regarding the relationship between sports participation, mental health and social outcomes at the community and elite level in adults. Based on the review findings, the secondary objective was to develop the ‘Mental Health through Sport’ conceptual model.

This review has been registered in the PROSPERO systematic review database and assigned the identifier: CRD42020185412. The conduct and reporting of this systematic review also follows the Preferred Reporting for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [ 30 ] (PRISMA flow diagram and PRISMA Checklist available in supplementary files ). This review is an update of a previous review of the same topic [ 31 ], published in 2012.

Identification of studies

Nine electronic databases (CINAHL, Cochrane Library, Google Scholar, Informit, Medline, PsychINFO, Psychology and Behavioural Sciences Collection, Scopus, and SPORTDiscus) were systematically searched for relevant records published from 2012 to March 10, 2020. The following key terms were developed by all members of the research team (and guided by previous reviews) and entered into these databases by author LW: sport* AND health AND value OR benefit* OR effect* OR outcome* OR impact* AND psych* OR depress* OR stress OR anxiety OR happiness OR mood OR ‘quality of life’ OR ‘social health’ OR ‘social relation*’ OR well* OR ‘social connect*’ OR ‘social functioning’ OR ‘life satisfac*’ OR ‘mental health’ OR social OR sociolog* OR affect* OR enjoy* OR fun. Where possible, Medical Subject Headings (MeSH) were also used.

Criteria for inclusion/exclusion

The titles of studies identified using this method were screened by LW. Abstract and full text of the articles were reviewed independently by LW and NE. To be included in the current review, each study needed to meet each of the following criteria: (1) published in English from 2012 to 2020; (2) full-text available online; (3) original research or report published in a peer-reviewed journal; (4) provides data on the psychological or social effects of participation in sport (with sport defined as a subset of exercise that can be undertaken individually or as a part of a team, where participants adhere to a common set of rules or expectations, and a defined goal exists); (5) the population of interest were adults (18 years and older) and were apparently healthy. All papers retrieved in the initial search were assessed for eligibility by title and abstract. In cases where a study could not be included or excluded via their title and abstract, the full text of the article was reviewed independently by two of the authors.

Data extraction

For the included studies, the following data was extracted independently by LW and checked by NE using a customized Google Docs spreadsheet: author name, year of publication, country, study design, aim, type of sport (e.g., tennis, hockey, team, individual), study conditions/comparisons, sample size, where participants were recruited from, mean age of participants, measure of sports participation, measure of physical activity, psychological and/or social outcome/s, measure of psychological and/or social outcome/s, statistical method of analysis, changes in physical activity or sports participation, and the psychological and/or social results.

Risk of bias (ROB) assessment

A risk of bias was performed by LW and AP independently using the ‘Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies’ OR the ‘Quality Assessment of Controlled Intervention Studies’ for the included quantitative studies, and the ‘Critical Appraisal Skills Programme (CASP) Checklist for the included qualitative studies [ 32 , 33 ]. Any discrepancies in the ROB assessments were discussed between the two reviewers, and a consensus reached.

The search yielded 8528 studies, with a total of 29 studies included in the systematic review (Fig.  1 ). Tables  1 and 2 provide a summary of the included studies. The research included adults from 18 to 84 years old, with most of the evidence coming from studies targeting young adults (18–25 years). Study samples ranged from 14 to 131, 962, with the most reported psychological outcomes being self-rated mental health ( n  = 5) and depression ( n  = 5). Most studies did not investigate or report the link between a particular sport and a specific mental health or social outcome; instead, the authors’ focused on comparing the impact of sport to physical activity, and/or individual sports compared to team sports. The results of this review are summarized in the following section, with findings presented by study design (cross-sectional, experimental, and longitudinal).

Flow of studies through the review process

Effects of sports participation on psychological well-being, ill-being, and social outcomes

Cross-sectional evidence.

This review included 14 studies reporting on the cross-sectional relationship between sports participation and psychological and/or social outcomes. Sample sizes range from n  = 414 to n  = 131,962 with a total of n  = 239,394 adults included across the cross-sectional studies.

The cross-sectional evidence generally supports that participation in sport, and especially team sports, is associated with greater mental health and psychological wellbeing in adults compared to non-participants [ 36 , 59 ]; and that higher frequency of sports participation and/or sport played at a higher level of competition, are also linked to lower levels of mental distress in adults . This was not the case for one specific study involving ice hockey players aged 35 and over, with Kitchen and Chowhan (2016) Kitchen and Chowhan (2016) reporting no relationship between participation in ice hockey and either mental health, or perceived life stress [ 54 ]. There is also some evidence to support that previous participation in sports (e.g., during childhood or young adulthood) is linked to better mental health outcomes later in life, including improved mental well-being and lower mental distress [ 59 ], even after controlling for age and current physical activity.

Compared to published community data for adults, elite or high-performance adult athletes demonstrated higher levels of body satisfaction, self-esteem, and overall life satisfaction [ 39 ]; and reported reduced tendency to respond to distress with anger and depression. However, rates of psychological distress were higher in the elite sport cohort (compared to community norms), with nearly 1 in 5 athletes reporting ‘high to very high’ distress, and 1 in 3 reporting poor mental health symptoms at a level warranting treatment by a health professional in one study ( n  = 749) [ 39 ].

Four studies focused on the associations between physical activity and sports participation and mental health outcomes in older adults. Physical activity was associated with greater quality of life [ 56 ], with the relationship strongest for those participating in sport in middle age, and for those who cycled in later life (> 65) [ 56 ]. Group physical activities (e.g., walking groups) and sports (e.g., golf) were also significantly related to excellent self-rated health, low depressive symptoms, high health-related quality of life (HRQoL) and a high frequency of laughter in males and females [ 60 , 61 ]. No participation or irregular participation in sport was associated with symptoms of mild to severe depression in older adults [ 62 ].

Several cross-sectional studies examined whether the effects of physical activity varied by type (e.g., total physical activity vs. sports participation). In an analysis of 1446 young adults (mean age = 18), total physical activity, moderate-to-vigorous physical activity, and team sport were independently associated with mental health [ 46 ]. Relative to individual physical activity, after adjusting for covariates and moderate-to-vigorous physical activity (MVPA), only team sport was significantly associated with improved mental health. Similarly, in a cross-sectional analysis of Australian women, Eime, Harvey, Payne (2014) reported that women who engaged in club and team-based sports (tennis or netball) reported better mental health and life satisfaction than those who engaged in individual types of physical activity [ 47 ]. Interestingly, there was no relationship between the amount of physical activity and either of these outcomes, suggesting that other qualities of sports participation contribute to its relationship to mental health and life satisfaction. There was also some evidence to support a relationship between exercise type (ball sports, aerobic activity, weightlifting, and dancing), and mental health amongst young adults (mean age 22 years) [ 48 ], with ball sports and dancing related to fewer symptoms of depression in students with high stress; and weightlifting related to fewer depressive symptoms in weightlifters exhibiting low stress.

Longitudinal evidence

Eight studies examined the longitudinal relationship between sports participation and either mental health and/or social outcomes. Sample sizes range from n  = 113 to n  = 1679 with a total of n  = 7022 adults included across the longitudinal studies.

Five of the included longitudinal studies focused on the relationship between sports participation in childhood or adolescence and mental health in young adulthood. There is evidence that participation in sport in high-school is protective of future symptoms of anxiety (including panic disorder, generalised anxiety disorder, social phobia, and agoraphobia) [ 42 ]. Specifically, after controlling for covariates (including current physical activity), the number of years of sports participation in high school was shown to be protective of symptoms of panic and agoraphobia in young adulthood, but not protective of symptoms of social phobia or generalized anxiety disorder [ 42 ]. A comparison of individual or team sports participation also revealed that participation in either context was protective of panic disorder symptoms, while only team sport was protective of agoraphobia symptoms, and only individual sport was protective of social phobia symptoms. Furthermore, current and past sports team participation was shown to negatively relate to adult depressive symptoms [ 43 ]; drop out of sport was linked to higher depressive symptoms in adulthood compared to those with maintained participation [ 9 , 22 , 63 ]; and consistent participation in team sports (but not individual sport) in adolescence was linked to higher self-rated mental health, lower perceived stress and depressive symptoms, and lower depression scores in early adulthood [ 53 , 58 ].

Two longitudinal studies [ 35 , 55 ], also investigated the association between team and individual playing context and mental health. Dore and colleagues [ 35 ] reported that compared to individual activities, being active in informal groups (e.g., yoga, running groups) or team sports was associated with better mental health, fewer depressive symptoms and higher social connectedness – and that involvement in team sports was related to better mental health regardless of physical activity volume. Kim and James [ 55 ] discovered that sports participation led to both short and long-term improvements in positive affect and life satisfaction.

A study on social outcomes related to mixed martial-arts (MMA) and Brazilian jiu-jitsu (BJJ) showed that both sports improved practitioners’ self-control and pro-social behavior, with greater improvements seen in the BJJ group [ 62 ]. Notably, while BJJ reduced participants’ reported aggression, there was a slight increase in MMA practitioners, though it is worth mentioning that individuals who sought out MMA had higher levels of baseline aggression.

Experimental evidence

Six of the included studies were experimental or quasi-experimental. Sample sizes ranged from n  = 28 to n  = 55 with a total of n  = 239 adults included across six longitudinal studies. Three studies involved a form of martial arts (such as judo and karate) [ 45 , 51 , 52 ], one involved a variety of team sports (such as netball, soccer, and cricket) [ 34 ], and the remaining two focused on badminton [ 57 ] and handball [ 49 ].

Brinkley and colleagues [ 34 ] reported significant effects on interpersonal communication (but not vitality, social cohesion, quality of life, stress, or interpersonal relationships) for participants ( n  = 40) engaging in a 12-week workplace team sports intervention. Also using a 12-week intervention, Hornstrup et al. [ 49 ] reported a significant improvement in mental energy (but not well-being or anxiety) in young women (mean age = 24; n  = 28) playing in a handball program. Patterns et al. [ 57 ] showed that in comparison to no exercise, participation in an 8-week badminton or running program had no significant improvement on self-esteem, despite improvements in perceived and actual fitness levels.

Three studies examined the effect of martial arts on the mental health of older adults (mean ages 79 [ 52 ], 64 [ 51 ], and 70 [ 45 ] years). Participation in Karate-Do had positive effects on overall mental health, emotional wellbeing, depression and anxiety when compared to other activities (physical, cognitive, mindfulness) and a control group [ 51 , 52 ]. Ciaccioni et al. [ 45 ] found that a Judo program did not affect either the participants’ mental health or their body satisfaction, citing a small sample size, and the limited length of the intervention as possible contributors to the findings.

Qualitative evidence

Three studies interviewed current or former sports players regarding their experiences with sport. Chinkov and Holt [ 41 ] reported that jiu-jitsu practitioners (mean age 35 years) were more self-confident in their lives outside of the gym, including improved self-confidence in their interactions with others because of their training. McGraw and colleagues [ 37 ] interviewed former and current National Football League (NFL) players and their families about its impact on the emotional and mental health of the players. Most of the players reported that their NFL career provided them with social and emotional benefits, as well as improvements to their self-esteem even after retiring. Though, despite these benefits, almost all the players experienced at least one mental health challenge during their career, including depression, anxiety, or difficulty controlling their temper. Some of the players and their families reported that they felt socially isolated from people outside of the national football league.

Through a series of semi-structured interviews and focus groups, Thorpe, Anders [ 40 ] investigated the impact of an Aboriginal male community sporting team on the health of its players. The players reported they felt a sense of belonging when playing in the team, further noting that the social and community aspects were as important as the physical health benefits. Participating in the club strengthened the cultural identity of the players, enhancing their well-being. The players further noted that participation provided them with enjoyment, stress relief, a sense of purpose, peer support, and improved self-esteem. Though they also noted challenges, including the presence of racism, community conflict, and peer-pressure.

Quality of studies

Full details of our risk of bias (ROB) results are provided in Supplementary Material A . Of the three qualitative studies assessed using the Critical Appraisal Skills Program (CASP), all three were deemed to have utilised and reported appropriate methodological standards on at least 8 of the 10 criteria. Twenty studies were assessed using the Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies, with all studies clearly reporting the research question/s or objective/s and study population. However, only four studies provided a justification for sample size, and less than half of the studies met quality criteria for items 6, 7, 9, or 10 (and items 12 and 13 were largely not applicable). Of concern, only four of the observational or cohort studies were deemed to have used clearly defined, valid, and reliable exposure measures (independent variables) and implemented them consistently across all study participants. Six studies were assessed using the Quality Assessment of Controlled Intervention Studies, with three studies described as a randomized trial (but none of the three reported a suitable method of randomization, concealment of treatment allocation, or blinding to treatment group assignment). Three studies showed evidence that study groups were similar at baseline for important characteristics and an overall drop-out rate from the study < 20%. Four studies reported high adherence to intervention protocols (with two not reporting) and five demonstrated that.study outcomes were assessed using valid and reliable measures and implemented consistently across all study participants. Importantly, researchers did not report or have access to validated instruments for assessing sport participation or physical activity amongst adults, though most studies provided psychometrics for their mental health outcome measure/s. Only one study reported that the sample size was sufficiently powered to detect a difference in the main outcome between groups (with ≥ 80% power) and that all participants were included in the analysis of results (intention-to-treat analysis). In general, the methodological quality of the six randomised studies was deemed low.

Initially, our discussion will focus on the review findings regarding sports participation and well-being, ill-being, and psychological health. However, the heterogeneity and methodological quality of the included research (especially controlled trials) should be considered during the interpretation of our results. Considering our findings, the Mental Health through Sport conceptual model for adults will then be presented and discussed and study limitations outlined.

Sports participation and psychological well-being

In summary, the evidence presented here indicates that for adults, sports participation is associated with better overall mental health [ 36 , 46 , 47 , 59 ], mood [ 56 ], higher life satisfaction [ 39 , 47 ], self-esteem [ 39 ], body satisfaction [ 39 ], HRQoL [ 60 ], self-rated health [ 61 ], and frequency of laughter [ 61 ]. Sports participation has also shown to be predictive of better psychological wellbeing over time [ 35 , 53 ], higher positive affect [ 55 ], and greater life satisfaction [ 55 ]. Furthermore, higher frequency of sports participation and/or sport played at a higher level of competition, have been linked to lower levels of mental distress, higher levels of body satisfaction, self-esteem, and overall life satisfaction in adults [ 39 ].

Despite considerable heterogeneity of sports type, cross-sectional and experimental research indicate that team-based sports participation, compared to individual sports and informal group physical activity, has a more positive effect on mental energy [ 49 ], physical self-perception [ 57 ], and overall psychological health and well-being in adults, regardless of physical activity volume [ 35 , 46 , 47 ]. And, karate-do benefits the subjective well-being of elderly practitioners [ 51 , 52 ]. Qualitative research in this area has queried participants’ experiences of jiu-jitsu, Australian football, and former and current American footballers. Participants in these sports reported that their participation was beneficial for psychological well-being [ 37 , 40 , 41 ], improved self-esteem [ 37 , 40 , 41 ], and enjoyment [ 37 ].

Sports participation and psychological ill-being

Of the included studies, n  = 19 examined the relationship between participating in sport and psychological ill-being. In summary, there is consistent evidence that sports participation is related to lower depression scores [ 43 , 48 , 61 , 62 ]. There were mixed findings regarding psychological stress, where participation in childhood (retrospectively assessed) was related to lower stress in young adulthood [ 41 ], but no relationship was identified between recreational hockey in adulthood and stress [ 54 ]. Concerning the potential impact of competing at an elite level, there is evidence of higher stress in elite athletes compared to community norms [ 39 ]. Further, there is qualitative evidence that many current or former national football league players experienced at least one mental health challenge, including depression, anxiety, difficulty controlling their temper, during their career [ 37 ].

Evidence from longitudinal research provided consistent evidence that participating in sport in adolescence is protective of symptoms of depression in young adulthood [ 43 , 53 , 58 , 63 ], and further evidence that participating in young adulthood is related to lower depressive symptoms over time (6 months) [ 35 ]. Participation in adolescence was also protective of manifestations of anxiety (panic disorder and agoraphobia) and stress in young adulthood [ 42 ], though participation in young adulthood was not related to a more general measure of anxiety [ 35 ] nor to changes in negative affect [ 55 ]). The findings from experimental research were mixed. Two studies examined the effect of karate-do on markers of psychological ill-being, demonstrating its capacity to reduce anxiety [ 52 ], with some evidence of its effectiveness on depression [ 51 ]. The other studies examined small-sided team-based games but showed no effect on stress or anxiety [ 34 , 49 ]. Most studies did not differentiate between team and individual sports, though one study found that adolescents who participated in team sports (not individual sports) in secondary school has lower depression scores in young adulthood [ 58 ].

Sports participation and social outcomes

Seven of the included studies examined the relationship between sports participation and social outcomes. However, very few studies examined social outcomes or tested a social outcome as a potential mediator of the relationship between sport and mental health. It should also be noted that this body of evidence comes from a wide range of sport types, including martial arts, professional football, and workplace team-sport, as well as different methodologies. Taken as a whole, the evidence shows that participating in sport is beneficial for several social outcomes, including self-control [ 50 ], pro-social behavior [ 50 ], interpersonal communication [ 34 ], and fostering a sense of belonging [ 40 ]. Further, there is evidence that group activity, for example team sport or informal group activity, is related to higher social connectedness over time, though analyses showed that social connectedness was not a mediator for mental health [ 35 ].

There were conflicting findings regarding social effects at the elite level, with current and former NFL players reporting that they felt socially isolated during their career [ 37 ], whilst another study reported no relationship between participation at the elite level and social dysfunction [ 39 ]. Conversely, interviews with a group of indigenous men revealed that they felt as though participating in an all-indigenous Australian football team provided them with a sense of purpose, and they felt as though the social aspect of the game was as important as the physical benefits it provides [ 40 ].

Mental health through sport conceptual model for adults

The ‘Health through Sport’ model provides a depiction of the determinants and benefits of sports participation [ 31 ]. The model recognises that the physical, mental, and social benefits of sports participation vary by the context of sport (e.g., individual vs. team, organized vs. informal). To identify the elements of sport which contribute to its effect on mental health outcomes, we describe the ‘Mental Health through Sport’ model (Fig.  2 ). The model proposes that the social and physical elements of sport each provide independent, and likely synergistic contributions to its overall influence on mental health.

The Mental Health through Sport conceptual model

The model describes two key pathways through which sport may influence mental health: physical activity, and social relationships and support. Several likely moderators of this effect are also provided, including sport type, intensity, frequency, context (team vs. individual), environment (e.g., indoor vs. outdoor), as well as the level of competition (e.g., elite vs. amateur).

The means by which the physical activity component of sport may influence mental health stems from the work of Lubans et al., who propose three key groups of mechanisms: neurobiological, psychosocial, and behavioral [ 64 ]. Processes whereby physical activity may enhance psychological outcomes via changes in the structural and functional composition of the brain are referred to as neurobiological mechanisms [ 65 , 66 ]. Processes whereby physical activity provides opportunities for the development of self-efficacy, opportunity for mastery, changes in self-perceptions, the development of independence, and for interaction with the environment are considered psychosocial mechanisms. Lastly, processes by which physical activity may influence behaviors which ultimately affect psychological health, including changes in sleep duration, self-regulation, and coping skills, are described as behavioral mechanisms.

Playing sport offers the opportunity to form relationships and to develop a social support network, both of which are likely to influence mental health. Thoits [ 29 ] describes 7 key mechanisms by which social relationships and support may influence mental health: social influence/social comparison; social control; role-based purpose and meaning (mattering); self-esteem; sense of control; belonging and companionship; and perceived support availability [ 29 ]. These mechanisms and their presence within a sporting context are elaborated below.

Subjective to the attitudes and behaviors of individuals in a group, social influence and comparison may facilitate protective or harmful effects on mental health. Participants in individual or team sport will be influenced and perhaps steered by the behaviors, expectations, and norms of other players and teams. When individual’s compare their capabilities, attitudes, and values to those of other participants, their own behaviors and subsequent health outcomes may be affected. When others attempt to encourage or discourage an individual to adopt or reject certain health practices, social control is displayed [ 29 ]. This may evolve as strategies between players (or between players and coach) are discussion and implemented. Likewise, teammates may try to motivate each another during a match to work harder, or to engage in specific events or routines off-field (fitness programs, after game celebrations, attending club events) which may impact current and future physical and mental health.

Sport may also provide behavioral guidance, purpose, and meaning to its participants. Role identities (positions within a social structure that come with reciprocal obligations), often formed as a consequence of social ties formed through sport. Particularly in team sports, participants come to understand they form an integral part of the larger whole, and consequently, they hold certain responsibility in ensuring the team’s success. They have a commitment to the team to, train and play, communicate with the team and a potential responsibility to maintain a high level of health, perform to their capacity, and support other players. As a source of behavioral guidance and of purpose and meaning in life, these identities are likely to influence mental health outcomes amongst sport participants.

An individual’s level of self-esteem may be affected by the social relationships and social support provided through sport; with improved perceptions of capability (or value within a team) in the sporting domain likely to have positive impact on global self-esteem and sense of worth [ 64 ]. The unique opportunities provided through participation in sport, also allow individuals to develop new skills, overcome challenges, and develop their sense of self-control or mastery . Working towards and finding creative solutions to challenges in sport facilitates a sense of mastery in participants. This sense of mastery may translate to other areas of life, with individual’s developing the confidence to cope with varied life challenges. For example, developing a sense of mastery regarding capacity to formulate new / creative solutions when taking on an opponent in sport may result in greater confidence to be creative at work. Social relationships and social support provided through sport may also provide participants with a source of belonging and companionship. The development of connections (on and off the field) to others who share common interests, can build a sense of belonging that may mediate improvements in mental health outcomes. Social support is often provided emotionally during expressions of trust and care; instrumentally via tangible assistance; through information such as advice and suggestions; or as appraisal such feedback. All forms of social support provided on and off the field contribute to a more generalised sense of perceived support that may mediate the effect of social interaction on mental health outcomes.

Participation in sport may influence mental health via some combination of the social mechanisms identified by Thoits, and the neurobiological, psychosocial, and behavioral mechanisms stemming from physical activity identified by Lubans [ 29 , 64 ]. The exact mechanisms through which sport may confer psychological benefit is likely to vary between sports, as each sport varies in its physical and social requirements. One must also consider the social effects of sports participation both on and off the field. For instance, membership of a sporting team and/or club may provide a sense of identity and belonging—an effect that persists beyond the immediacy of playing the sport and may have a persistent effect on their psychological health. Furthermore, the potential for team-based activity to provide additional benefit to psychological outcomes may not just be attributable to the differences in social interactions, there are also physiological differences in the requirements for sport both within (team vs. team) and between (team vs. individual) categories that may elicit additional improvements in psychological outcomes. For example, evidence supports that exercise intensity moderates the relationship between physical activity and several psychological outcomes—supporting that sports performed at higher intensity will be more beneficial for psychological health.

Limitations and recommendations

There are several limitations of this review worthy of consideration. Firstly, amongst the included studies there was considerable heterogeneity in study outcomes and study methodology, and self-selection bias (especially in non-experimental studies) is likely to influence study findings and reduce the likelihood that study participants and results are representative of the overall population. Secondly, the predominately observational evidence included in this and Eime’s prior review enabled us to identify the positive relationship between sports participation and social and psychological health (and examine directionality)—but more experimental and longitudinal research is required to determine causality and explore potential mechanisms responsible for the effect of sports participation on participant outcomes. Additional qualitative work would also help researchers gain a better understanding of the relationship between specific elements of the sporting environment and mental health and social outcomes in adult participants. Thirdly, there were no studies identified in the literature where sports participation involved animals (such as equestrian sports) or guns (such as shooting sports). Such studies may present novel and important variables in the assessment of mental health benefits for participants when compared to non-participants or participants in sports not involving animals/guns—further research is needed in this area. Our proposed conceptual model also identifies several pathways through which sport may lead to improvements in mental health—but excludes some potentially negative influences (such as poor coaching behaviors and injury). And our model is not designed to capture all possible mechanisms, creating the likelihood that other mechanisms exist but are not included in this review. Additionally, an interrelationship exits between physical activity, mental health, and social relationships, whereby changes in one area may facilitate changes in the other/s; but for the purpose of this study, we have focused on how the physical and social elements of sport may mediate improvements in psychological outcomes. Consequently, our conceptual model is not all-encompassing, but designed to inform and guide future research investigating the impact of sport participation on mental health.

The findings of this review endorse that participation in sport is beneficial for psychological well-being, indicators of psychological ill-being, and social outcomes in adults. Furthermore, participation in team sports is associated with better psychological and social outcomes compared to individual sports or other physical activities. Our findings support and add to previous review findings [ 1 ]; and have informed the development of our ‘Mental Health through Sport’ conceptual model for adults which presents the potential mechanisms by which participation in sport may affect mental health.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

We would like to acknowledge the work of the original systematic review conducted by Eime, R. M., Young, J. A., Harvey, J. T., Charity, M. J., and Payne, W. R. (2013).

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All authors contributed to the conducting of this study and reporting the findings. The titles of studies identified were screened by LW, and abstracts and full text articles reviewed independently by LW and NE. For the included studies, data was extracted independently by LW and checked by NE, and the risk of bias assessment was performed by LW and AP independently. All authors have read and approved the final version of the manuscript and agree with the order of presentation of the authors.

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Eather, N., Wade, L., Pankowiak, A. et al. The impact of sports participation on mental health and social outcomes in adults: a systematic review and the ‘Mental Health through Sport’ conceptual model. Syst Rev 12 , 102 (2023). https://doi.org/10.1186/s13643-023-02264-8

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Sport psychology and performance meta-analyses: A systematic review of the literature

Roles Conceptualization, Data curation, Formal analysis, Methodology, Project administration, Supervision, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliations Department of Kinesiology and Sport Management, Texas Tech University, Lubbock, Texas, United States of America, Education Academy, Vytautas Magnus University, Kaunas, Lithuania

Roles Data curation, Methodology, Writing – original draft

Affiliation Department of Psychological Sciences, Texas Tech University, Lubbock, Texas, United States of America

Roles Data curation, Methodology

Roles Writing – original draft, Writing – review & editing

Affiliation Department of Kinesiology and Sport Management, Honors College, Texas Tech University, Lubbock, Texas, United States of America

Roles Data curation, Methodology, Writing – original draft, Writing – review & editing

Affiliation Faculty of Education, Health and Well-Being, University of Wolverhampton, Walsall, West Midlands, United Kingdom

Roles Data curation, Formal analysis, Methodology, Writing – original draft, Writing – review & editing

Affiliation Division of Research & Innovation, University of Southern Queensland, Toowoomba, Queensland, Australia

• Marc Lochbaum, 
• Elisabeth Stoner, 
• Tristen Hefner, 
• Sydney Cooper, 
• Andrew M. Lane, 
• Peter C. Terry

• Published: February 16, 2022
• https://doi.org/10.1371/journal.pone.0263408
• Peer Review
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Sport psychology as an academic pursuit is nearly two centuries old. An enduring goal since inception has been to understand how psychological techniques can improve athletic performance. Although much evidence exists in the form of meta-analytic reviews related to sport psychology and performance, a systematic review of these meta-analyses is absent from the literature. We aimed to synthesize the extant literature to gain insights into the overall impact of sport psychology on athletic performance. Guided by the PRISMA statement for systematic reviews, we reviewed relevant articles identified via the EBSCOhost interface. Thirty meta-analyses published between 1983 and 2021 met the inclusion criteria, covering 16 distinct sport psychology constructs. Overall, sport psychology interventions/variables hypothesized to enhance performance (e.g., cohesion, confidence, mindfulness) were shown to have a moderate beneficial effect ( d = 0.51), whereas variables hypothesized to be detrimental to performance (e.g., cognitive anxiety, depression, ego climate) had a small negative effect ( d = -0.21). The quality rating of meta-analyses did not significantly moderate the magnitude of observed effects, nor did the research design (i.e., intervention vs. correlation) of the primary studies included in the meta-analyses. Our review strengthens the evidence base for sport psychology techniques and may be of great practical value to practitioners. We provide recommendations for future research in the area.

Citation: Lochbaum M, Stoner E, Hefner T, Cooper S, Lane AM, Terry PC (2022) Sport psychology and performance meta-analyses: A systematic review of the literature. PLoS ONE 17(2): e0263408. https://doi.org/10.1371/journal.pone.0263408

Editor: Claudio Imperatori, European University of Rome, ITALY

Received: September 28, 2021; Accepted: January 18, 2022; Published: February 16, 2022

Copyright: © 2022 Lochbaum et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper.

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Sport performance matters. Verifying its global importance requires no more than opening a newspaper to the sports section, browsing the internet, looking at social media outlets, or scanning abundant sources of sport information. Sport psychology is an important avenue through which to better understand and improve sport performance. To date, a systematic review of published sport psychology and performance meta-analyses is absent from the literature. Given the undeniable importance of sport, the history of sport psychology in academics since 1830, and the global rise of sport psychology journals and organizations, a comprehensive systematic review of the meta-analytic literature seems overdue. Thus, we aimed to consolidate the existing literature and provide recommendations for future research.

The development of sport psychology

The history of sport psychology dates back nearly 200 years. Terry [ 1 ] cites Carl Friedrich Koch’s (1830) publication titled [in translation] Calisthenics from the Viewpoint of Dietetics and Psychology [ 2 ] as perhaps the earliest publication in the field, and multiple commentators have noted that sport psychology experiments occurred in the world’s first psychology laboratory, established by Wilhelm Wundt at the University of Leipzig in 1879 [ 1 , 3 ]. Konrad Rieger’s research on hypnosis and muscular endurance, published in 1884 [ 4 ] and Angelo Mosso’s investigations of the effects of mental fatigue on physical performance, published in 1891 [ 5 ] were other early landmarks in the development of applied sport psychology research. Following the efforts of Koch, Wundt, Rieger, and Mosso, sport psychology works appeared with increasing regularity, including Philippe Tissié’s publications in 1894 [ 6 , 7 ] on psychology and physical training, and Pierre de Coubertin’s first use of the term sport psychology in his La Psychologie du Sport paper in 1900 [ 8 ]. In short, the history of sport psychology and performance research began as early as 1830 and picked up pace in the latter part of the 19 th century. Early pioneers, who helped shape sport psychology include Wundt, recognized as the “father of experimental psychology”, Tissié, the founder of French physical education and Legion of Honor awardee in 1932, and de Coubertin who became the father of the modern Olympic movement and founder of the International Olympic Committee.

Sport psychology flourished in the early 20 th century [see 1, 3 for extensive historic details]. For instance, independent laboratories emerged in Berlin, Germany, established by Carl Diem in 1920; in St. Petersburg and Moscow, Russia, established respectively by Avksenty Puni and Piotr Roudik in 1925; and in Champaign, Illinois USA, established by Coleman Griffith, also in 1925. The period from 1950–1980 saw rapid strides in sport psychology, with Franklin Henry establishing this field of study as independent of physical education in the landscape of American and eventually global sport science and kinesiology graduate programs [ 1 ]. In addition, of great importance in the 1960s, three international sport psychology organizations were established: namely, the International Society for Sport Psychology (1965), the North American Society for the Psychology of Sport and Physical Activity (1966), and the European Federation of Sport Psychology (1969). Since that time, the Association of Applied Sport Psychology (1986), the South American Society for Sport Psychology (1986), and the Asian-South Pacific Association of Sport Psychology (1989) have also been established.

The global growth in academic sport psychology has seen a large number of specialist publications launched, including the following journals: International Journal of Sport Psychology (1970), Journal of Sport & Exercise Psychology (1979), The Sport Psychologist (1987), Journal of Applied Sport Psychology (1989), Psychology of Sport and Exercise (2000), International Journal of Sport and Exercise Psychology (2003), Journal of Clinical Sport Psychology (2007), International Review of Sport and Exercise Psychology (2008), Journal of Sport Psychology in Action (2010), Sport , Exercise , and Performance Psychology (2014), and the Asian Journal of Sport & Exercise Psychology (2021).

In turn, the growth in journal outlets has seen sport psychology publications burgeon. Indicative of the scale of the contemporary literature on sport psychology, searches completed in May 2021 within the Web of Science Core Collection, identified 1,415 publications on goal setting and sport since 1985; 5,303 publications on confidence and sport since 1961; and 3,421 publications on anxiety and sport since 1980. In addition to academic journals, several comprehensive edited textbooks have been produced detailing sport psychology developments across the world, such as Hanrahan and Andersen’s (2010) Handbook of Applied Sport Psychology [ 9 ], Schinke, McGannon, and Smith’s (2016) International Handbook of Sport Psychology [ 10 ], and Bertollo, Filho, and Terry’s (2021) Advancements in Mental Skills Training [ 11 ] to name just a few. In short, sport psychology is global in both academic study and professional practice.

Meta-analysis in sport psychology

Several meta-analysis guides, computer programs, and sport psychology domain-specific primers have been popularized in the social sciences [ 12 , 13 ]. Sport psychology academics have conducted quantitative reviews on much studied constructs since the 1980s, with the first two appearing in 1983 in the form of Feltz and Landers’ meta-analysis on mental practice [ 14 ], which included 98 articles dating from 1934, and Bond and Titus’ cross-disciplinary meta-analysis on social facilitation [ 15 ], which summarized 241 studies including Triplett’s (1898) often-cited study of social facilitation in cycling [ 16 ]. Although much meta-analytic evidence exists for various constructs in sport and exercise psychology [ 12 ] including several related to performance [ 17 ], the evidence is inconsistent. For example, two meta-analyses, both ostensibly summarizing evidence of the benefits to performance of task cohesion [ 18 , 19 ], produced very different mean effects ( d = .24 vs d = 1.00) indicating that the true benefit lies somewhere in a wide range from small to large. Thus, the lack of a reliable evidence base for the use of sport psychology techniques represents a significant gap in the knowledge base for practitioners and researchers alike. A comprehensive systematic review of all published meta-analyses in the field of sport psychology has yet to be published.

Purpose and aim

We consider this review to be both necessary and long overdue for the following reasons: (a) the extensive history of sport psychology and performance research; (b) the prior publication of many meta-analyses summarizing various aspects of sport psychology research in a piecemeal fashion [ 12 , 17 ] but not its totality; and (c) the importance of better understanding and hopefully improving sport performance via the use of interventions based on solid evidence of their efficacy. Hence, we aimed to collate and evaluate this literature in a systematic way to gain improved understanding of the impact of sport psychology variables on sport performance by construct, research design, and meta-analysis quality, to enhance practical knowledge of sport psychology techniques and identify future lines of research inquiry. By systematically reviewing all identifiable meta-analytic reviews linking sport psychology techniques with sport performance, we aimed to evaluate the strength of the evidence base underpinning sport psychology interventions.

Materials and methods

This systematic review of meta-analyses followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [ 20 ]. We did not register our systematic review protocol in a database. However, we specified our search strategy, inclusion criteria, data extraction, and data analyses in advance of writing our manuscript. All details of our work are available from the lead author. Concerning ethics, this systematic review received a waiver from Texas Tech University Human Subject Review Board as it concerned archival data (i.e., published meta-analyses).

Eligibility criteria

Published meta-analyses were retained for extensive examination if they met the following inclusion criteria: (a) included meta-analytic data such as mean group, between or within-group differences or correlates; (b) published prior to January 31, 2021; (c) published in a peer-reviewed journal; (d) investigated a recognized sport psychology construct; and (e) meta-analyzed data concerned with sport performance. There was no language of publication restriction. To align with our systematic review objectives, we gave much consideration to study participants and performance outcomes. Across multiple checks, all authors confirmed study eligibility. Three authors (ML, AL, and PT) completed the final inclusion assessments.

Information sources

Authors searched electronic databases, personal meta-analysis history, and checked with personal research contacts. Electronic database searches occurred in EBSCOhost with the following individual databases selected: APA PsycINFO, ERIC, Psychology and Behavioral Sciences Collection, and SPORTDiscus. An initial search concluded October 1, 2020. ML, AL, and PT rechecked the identified studies during the February–March, 2021 period, which resulted in the identification of two additional meta-analyses [ 21 , 22 ].

Search protocol

ML and ES initially conducted independent database searches. For the first search, ML used the following search terms: sport psychology with meta-analysis or quantitative review and sport and performance or sport* performance. For the second search, ES utilized a sport psychology textbook and used the chapter title terms (e.g., goal setting). In EBSCOhost, both searches used the advanced search option that provided three separate boxes for search terms such as box 1 (sport psychology), box 2 (meta-analysis), and box 3 (performance). Specific details of our search strategy were:

Search by ML:

• sport psychology, meta-analysis, sport and performance
• sport psychology, meta-analysis or quantitative review, sport* performance
• sport psychology, quantitative review, sport and performance
• sport psychology, quantitative review, sport* performance

Search by ES:

• mental practice or mental imagery or mental rehearsal and sports performance and meta-analysis
• goal setting and sports performance and meta-analysis
• anxiety and stress and sports performance and meta-analysis
• competition and sports performance and meta-analysis
• diversity and sports performance and meta-analysis
• cohesion and sports performance and meta-analysis
• imagery and sports performance and meta-analysis
• self-confidence and sports performance and meta-analysis
• concentration and sports performance and meta-analysis
• athletic injuries and sports performance and meta-analysis
• overtraining and sports performance and meta-analysis
• children and sports performance and meta-analysis

The following specific search of the EBSCOhost with SPORTDiscus, APA PsycINFO, Psychology and Behavioral Sciences Collection, and ERIC databases, returned six results from 2002–2020, of which three were included [ 18 , 19 , 23 ] and three were excluded because they were not meta-analyses.

• Box 1 cohesion
• Box 2 sports performance
• Box 3 meta-analysis

Study selection

As detailed in the PRISMA flow chart ( Fig 1 ) and the specified inclusion criteria, a thorough study selection process was used. As mentioned in the search protocol, two authors (ML and ES) engaged independently with two separate searches and then worked together to verify the selected studies. Next, AL and PT examined the selected study list for accuracy. ML, AL, and PT, whilst rating the quality of included meta-analyses, also re-examined all selected studies to verify that each met the predetermined study inclusion criteria. Throughout the study selection process, disagreements were resolved through discussion until consensus was reached.

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

https://doi.org/10.1371/journal.pone.0263408.g001

Data extraction process

Initially, ML, TH, and ES extracted data items 1, 2, 3 and 8 (see Data items). Subsequently, ML, AL, and PT extracted the remaining data (items 4–7, 9, 10). Checks occurred during the extraction process for potential discrepancies (e.g., checking the number of primary studies in a meta-analysis). It was unnecessary to contact any meta-analysis authors for missing information or clarification during the data extraction process because all studies reported the required information. Across the search for meta-analyses, all identified studies were reported in English. Thus, no translation software or searching out a native speaker occurred. All data extraction forms (e.g., data items and individual meta-analysis quality) are available from the first author.

To help address our main aim, we extracted the following information from each meta-analysis: (1) author(s); (2) publication year; (3) construct(s); (4) intervention based meta-analysis (yes, no, mix); (5) performance outcome(s) description; (6) number of studies for the performance outcomes; (7) participant description; (8) main findings; (9) bias correction method/results; and (10) author(s) stated conclusions. For all information sought, we coded missing information as not reported.

Individual meta-analysis quality

ML, AL, and PT independently rated the quality of individual meta-analysis on the following 25 points found in the PRISMA checklist [ 20 ]: title; abstract structured summary; introduction rationale, objectives, and protocol and registration; methods eligibility criteria, information sources, search, study selection, data collection process, data items, risk of bias of individual studies, summary measures, synthesis of results, and risk of bias across studies; results study selection, study characteristics, risk of bias within studies, results of individual studies, synthesis of results, and risk of bias across studies; discussion summary of evidence, limitations, and conclusions; and funding. All meta-analyses were rated for quality by two coders to facilitate inter-coder reliability checks, and the mean quality ratings were used in subsequent analyses. One author (PT), having completed his own ratings, received the incoming ratings from ML and AL and ran the inter-coder analysis. Two rounds of ratings occurred due to discrepancies for seven meta-analyses, mainly between ML and AL. As no objective quality categorizations (i.e., a point system for grouping meta-analyses as poor, medium, good) currently exist, each meta-analysis was allocated a quality score of up to a maximum of 25 points. All coding records are available upon request.

Planned methods of analysis

Several preplanned methods of analysis occurred. We first assessed the mean quality rating of each meta-analysis based on our 25-point PRISMA-based rating system. Next, we used a median split of quality ratings to determine whether standardized mean effects (SMDs) differed by the two formed categories, higher and lower quality meta-analyses. Meta-analysis authors reported either of two different effect size metrics (i.e., r and SMD); hence we converted all correlational effects to SMD (i.e., Cohen’s d ) values using an online effect size calculator ( www.polyu.edu.hk/mm/effectsizefaqs/calculator/calculator.html ). We interpreted the meaningfulness of effects based on Cohen’s interpretation [ 24 ] with 0.20 as small, 0.50 as medium, 0.80 as large, and 1.30 as very large. As some psychological variables associate negatively with performance (e.g., confusion [ 25 ], cognitive anxiety [ 26 ]) whereas others associate positively (e.g., cohesion [ 23 ], mental practice [ 14 ]), we grouped meta-analyses according to whether the hypothesized effect with performance was positive or negative, and summarized the overall effects separately. By doing so, we avoided a scenario whereby the demonstrated positive and negative effects canceled one another out when combined. The effect of somatic anxiety on performance, which is hypothesized to follow an inverted-U relationship, was categorized as neutral [ 35 ]. Last, we grouped the included meta-analyses according to whether the primary studies were correlational in nature or involved an intervention and summarized these two groups of meta-analyses separately.

Study characteristics

Table 1 contains extracted data from 30 meta-analyses meeting the inclusion criteria, dating from 1983 [ 14 ] to 2021 [ 21 ]. The number of primary studies within the meta-analyses ranged from three [ 27 ] to 109 [ 28 ]. In terms of the description of participants included in the meta-analyses, 13 included participants described simply as athletes, whereas other meta-analyses identified a mix of elite athletes (e.g., professional, Olympic), recreational athletes, college-aged volunteers (many from sport science departments), younger children to adolescents, and adult exercisers. Of the 30 included meta-analyses, the majority ( n = 18) were published since 2010. The decadal breakdown of meta-analyses was 1980–1989 ( n = 1 [ 14 ]), 1990–1999 ( n = 6 [ 29 – 34 ]), 2000–2009 ( n = 5 [ 23 , 25 , 26 , 35 , 36 ]), 2010–2019 ( n = 12 [ 18 , 19 , 22 , 27 , 37 – 43 , 48 ]), and 2020–2021 ( n = 6 [ 21 , 28 , 44 – 47 ]).

https://doi.org/10.1371/journal.pone.0263408.t001

As for the constructs covered, we categorized the 30 meta-analyses into the following areas: mental practice/imagery [ 14 , 29 , 30 , 42 , 46 , 47 ], anxiety [ 26 , 31 , 32 , 35 ], confidence [ 26 , 35 , 36 ], cohesion [ 18 , 19 , 23 ], goal orientation [ 22 , 44 , 48 ], mood [ 21 , 25 , 34 ], emotional intelligence [ 40 ], goal setting [ 33 ], interventions [ 37 ], mindfulness [ 27 ], music [ 28 ], neurofeedback training [ 43 ], perfectionism [ 39 ], pressure training [ 45 ], quiet eye training [ 41 ], and self-talk [ 38 ]. Multiple effects were generated from meta-analyses that included more than one construct (e.g., tension, depression, etc. [ 21 ]; anxiety and confidence [ 26 ]). In relation to whether the meta-analyses included in our review assessed the effects of a sport psychology intervention on performance or relationships between psychological constructs and performance, 13 were intervention-based, 14 were correlational, two included a mix of study types, and one included a large majority of cross-sectional studies ( Table 1 ).

A wide variety of performance outcomes across many sports was evident, such as golf putting, dart throwing, maximal strength, and juggling; or categorical outcomes such as win/loss and Olympic team selection. Given the extensive list of performance outcomes and the incomplete descriptions provided in some meta-analyses, a clear categorization or count of performance types was not possible. Sufficient to conclude, researchers utilized many performance outcomes across a wide range of team and individual sports, motor skills, and strength and aerobic tasks.

Effect size data and bias correction

To best summarize the effects, we transformed all correlations to SMD values (i.e., Cohen’s d ). Across all included meta-analyses shown in Table 2 and depicted in Fig 2 , we identified 61 effects. Having corrected for bias, effect size values were assessed for meaningfulness [ 24 ], which resulted in 15 categorized as negligible (< ±0.20), 29 as small (±0.20 to < 0.50), 13 as moderate (±0.50 to < 0.80), 2 as large (±0.80 to < 1.30), and 1 as very large (≥ 1.30).

https://doi.org/10.1371/journal.pone.0263408.g002

https://doi.org/10.1371/journal.pone.0263408.t002

Study quality rating results and summary analyses

Following our PRISMA quality ratings, intercoder reliability coefficients were initially .83 (ML, AL), .95 (ML, PT), and .90 (AL, PT), with a mean intercoder reliability coefficient of .89. To achieve improved reliability (i.e., r mean > .90), ML and AL re-examined their ratings. As a result, intercoder reliability increased to .98 (ML, AL), .96 (ML, PT), and .92 (AL, PT); a mean intercoder reliability coefficient of .95. Final quality ratings (i.e., the mean of two coders) ranged from 13 to 25 ( M = 19.03 ± 4.15). Our median split into higher ( M = 22.83 ± 1.08, range 21.5–25, n = 15) and lower ( M = 15.47 ± 2.42, range 13–20.5, n = 15) quality groups produced significant between-group differences in quality ( F 1,28 = 115.62, p < .001); hence, the median split met our intended purpose. The higher quality group of meta-analyses were published from 2015–2021 (median 2018) and the lower quality group from 1983–2014 (median 2000). It appears that meta-analysis standards have risen over the years since the PRISMA criteria were first introduced in 2009. All data for our analyses are shown in Table 2 .

Table 3 contains summary statistics with bias-corrected values used in the analyses. The overall mean effect for sport psychology constructs hypothesized to have a positive impact on performance was of moderate magnitude ( d = 0.51, 95% CI = 0.42, 0.58, n = 36). The overall mean effect for sport psychology constructs hypothesized to have a negative impact on performance was small in magnitude ( d = -0.21, 95% CI -0.31, -0.11, n = 24). In both instances, effects were larger, although not significantly so, among meta-analyses of higher quality compared to those of lower quality. Similarly, mean effects were larger but not significantly so, where reported effects in the original studies were based on interventional rather than correlational designs. This trend only applied to hypothesized positive effects because none of the original studies in the meta-analyses related to hypothesized negative effects used interventional designs.

https://doi.org/10.1371/journal.pone.0263408.t003

In this systematic review of meta-analyses, we synthesized the available evidence regarding effects of sport psychology interventions/constructs on sport performance. We aimed to consolidate the literature, evaluate the potential for meta-analysis quality to influence the results, and suggest recommendations for future research at both the single study and quantitative review stages. During the systematic review process, several meta-analysis characteristics came to light, such as the number of meta-analyses of sport psychology interventions (experimental designs) compared to those summarizing the effects of psychological constructs (correlation designs) on performance, the number of meta-analyses with exclusively athletes as participants, and constructs featuring in multiple meta-analyses, some of which (e.g., cohesion) produced very different effect size values. Thus, although our overall aim was to evaluate the strength of the evidence base for use of psychological interventions in sport, we also discuss the impact of these meta-analysis characteristics on the reliability of the evidence.

When seen collectively, results of our review are supportive of using sport psychology techniques to help improve performance and confirm that variations in psychological constructs relate to variations in performance. For constructs hypothesized to have a positive effect on performance, the mean effect strength was moderate ( d = 0.51) although there was substantial variation between constructs. For example, the beneficial effects on performance of task cohesion ( d = 1.00) and self-efficacy ( d = 0.82) are large, and the available evidence base for use of mindfulness interventions suggests a very large beneficial effect on performance ( d = 1.35). Conversely, some hypothetically beneficial effects (2 of 36; 5.6%) were in the negligible-to-small range (0.15–0.20) and most beneficial effects (19 of 36; 52.8%) were in the small-to-moderate range (0.22–0.49). It should be noted that in the world of sport, especially at the elite level, even a small beneficial effect on performance derived from a psychological intervention may prove the difference between success and failure and hence small effects may be of great practical value. To put the scale of the benefits into perspective, an authoritative and extensively cited review of healthy eating and physical activity interventions [ 49 ] produced an overall pooled effect size of 0.31 (compared to 0.51 for our study), suggesting sport psychology interventions designed to improve performance are generally more effective than interventions designed to promote healthy living.

Among hypothetically negative effects (e.g., ego climate, cognitive anxiety, depression), the mean detrimental effect was small ( d = -0.21) although again substantial variation among constructs was evident. Some hypothetically negative constructs (5 of 24; 20.8%) were found to actually provide benefits to performance, albeit in the negligible range (0.02–0.12) and only two constructs (8.3%), both from Lochbaum and colleagues’ POMS meta-analysis [ 21 ], were shown to negatively affect performance above a moderate level (depression: d = -0.64; total mood disturbance, which incorporates the depression subscale: d = -0.84). Readers should note that the POMS and its derivatives assess six specific mood dimensions rather than the mood construct more broadly, and therefore results should not be extrapolated to other dimensions of mood [ 50 ].

Mean effects were larger among higher quality than lower quality meta-analyses for both hypothetically positive ( d = 0.54 vs d = 0.45) and negative effects ( d = -0.25 vs d = 0.17), but in neither case were the differences significant. It is reasonable to assume that the true effects were derived from the higher quality meta-analyses, although our conclusions remain the same regardless of study quality. Overall, our findings provide a more rigorous evidence base for the use of sport psychology techniques by practitioners than was previously available, representing a significant contribution to knowledge. Moreover, our systematic scrutiny of 30 meta-analyses published between 1983 and 2021 has facilitated a series of recommendations to improve the quality of future investigations in the sport psychology area.

Recommendations

The development of sport psychology as an academic discipline and area of professional practice relies on using evidence and theory to guide practice. Hence, a strong evidence base for the applied work of sport psychologists is of paramount importance. Although the beneficial effects of some sport psychology techniques are small, it is important to note the larger performance benefits for other techniques, which may be extremely meaningful for applied practice. Overall, however, especially given the heterogeneity of the observed effects, it would be wise for applied practitioners to avoid overpromising the benefits of sport psychology services to clients and perhaps underdelivering as a result [ 1 ].

The results of our systematic review can be used to generate recommendations for how the profession might conduct improved research to better inform applied practice. Much of the early research in sport psychology was exploratory and potential moderating variables were not always sufficiently controlled. Terry [ 51 ] outlined this in relation to the study of mood-performance relationships, identifying that physical and skills factors will very likely exert a greater influence on performance than psychological factors. Further, type of sport (e.g., individual vs. team), duration of activity (e.g., short vs. long duration), level of competition (e.g., elite vs. recreational), and performance measure (e.g., norm-referenced vs. self-referenced) have all been implicated as potential moderators of the relationship between psychological variables and sport performance [ 51 ]. To detect the relatively subtle effects of psychological effects on performance, research designs need to be sufficiently sensitive to such potential confounds. Several specific methodological issues are worth discussing.

The first issue relates to measurement. Investigating the strength of a relationship requires the measured variables to be valid, accurate and reliable. Psychological variables in the meta-analyses we reviewed relied primarily on self-report outcome measures. The accuracy of self-report data requires detailed inner knowledge of thoughts, emotions, and behavior. Research shows that the accuracy of self-report information is subject to substantial individual differences [ 52 , 53 ]. Therefore, self-report data, at best, are an estimate of the measure. Measurement issues are especially relevant to the assessment of performance, and considerable measurement variation was evident between meta-analyses. Some performance measures were more sensitive, especially those assessing physical performance relative to what is normal for the individual performer (i.e., self-referenced performance). Hence, having multiple baseline indicators of performance increases the probability of identifying genuine performance enhancement derived from a psychological intervention [ 54 ].

A second issue relates to clarifying the rationale for how and why specific psychological variables might influence performance. A comprehensive review of prerequisites and precursors of athletic talent [ 55 ] concluded that the superiority of Olympic champions over other elite athletes is determined in part by a range of psychological variables, including high intrinsic motivation, determination, dedication, persistence, and creativity, thereby identifying performance-related variables that might benefit from a psychological intervention. Identifying variables that influence the effectiveness of interventions is a challenging but essential issue for researchers seeking to control and assess factors that might influence results [ 49 ]. A key part of this process is to use theory to propose the mechanism(s) by which an intervention might affect performance and to hypothesize how large the effect might be.

A third issue relates to the characteristics of the research participants involved. Out of convenience, it is not uncommon for researchers to use undergraduate student participants for research projects, which may bias results and restrict the generalization of findings to the population of primary interest, often elite athletes. The level of training and physical conditioning of participants will clearly influence their performance. Highly trained athletes will typically make smaller gains in performance over time than novice athletes, due to a ceiling effect (i.e., they have less room for improvement). For example, consider runner A, who takes 20 minutes to run 5km one week but 19 minutes the next week, and Runner B who takes 30 minutes one week and 25 minutes the next. If we compare the two, Runner A runs faster than Runner B on both occasions, but Runner B improved more, so whose performance was better? If we also consider Runner C, a highly trained athlete with a personal best of 14 minutes, to run 1 minute quicker the following week would almost require a world record time, which is clearly unlikely. For this runner, an improvement of a few seconds would represent an excellent performance. Evidence shows that trained, highly motivated athletes may reach performance plateaus and as such are good candidates for psychological skills training. They are less likely to make performance gains due to increased training volume and therefore the impact of psychological skills interventions may emerge more clearly. Therefore, both test-retest and cross-sectional research designs should account for individual difference variables. Further, the range of individual difference factors will be context specific; for example, individual differences in strength will be more important in a study that uses weightlifting as the performance measure than one that uses darts as the performance measure, where individual differences in skill would be more important.

A fourth factor that has not been investigated extensively relates to the variables involved in learning sport psychology techniques. Techniques such as imagery, self-talk and goal setting all require cognitive processing and as such some people will learn them faster than others [ 56 ]. Further, some people are intuitive self-taught users of, for example, mood regulation strategies such as abdominal breathing or listening to music who, if recruited to participate in a study investigating the effects of learning such techniques on performance, would respond differently to novice users. Hence, a major challenge when testing the effects of a psychological intervention is to establish suitable controls. A traditional non-treatment group offers one option, but such an approach does not consider the influence of belief effects (i.e., placebo/nocebo), which can either add or detract from the effectiveness of performance interventions [ 57 ]. If an individual believes that, an intervention will be effective, this provides a motivating effect for engagement and so performance may improve via increased effort rather than the effect of the intervention per se.

When there are positive beliefs that an intervention will work, it becomes important to distinguish belief effects from the proposed mechanism through which the intervention should be successful. Research has shown that field studies often report larger effects than laboratory studies, a finding attributed to higher motivation among participants in field studies [ 58 ]. If participants are motivated to improve, being part of an active training condition should be associated with improved performance regardless of any intervention. In a large online study of over 44,000 participants, active training in sport psychology interventions was associated with improved performance, but only marginally more than for an active control condition [ 59 ]. The study involved 4-time Olympic champion Michael Johnson narrating both the intervention and active control using motivational encouragement in both conditions. Researchers should establish not only the expected size of an effect but also to specify and assess why the intervention worked. Where researchers report performance improvement, it is fundamental to explain the proposed mechanism by which performance was enhanced and to test the extent to which the improvement can be explained by the proposed mechanism(s).

Limitations

Systematic reviews are inherently limited by the quality of the primary studies included. Our review was also limited by the quality of the meta-analyses that had summarized the primary studies. We identified the following specific limitations; (1) only 12 meta-analyses summarized primary studies that were exclusively intervention-based, (2) the lack of detail regarding control groups in the intervention meta-analyses, (3) cross-sectional and correlation-based meta-analyses by definition do not test causation, and therefore provide limited direct evidence of the efficacy of interventions, (4) the extensive array of performance measures even within a single meta-analysis, (5) the absence of mechanistic explanations for the observed effects, and (6) an absence of detail across intervention-based meta-analyses regarding number of sessions, participants’ motivation to participate, level of expertise, and how the intervention was delivered. To ameliorate these concerns, we included a quality rating for all included meta-analyses. Having created higher and lower quality groups using a median split of quality ratings, we showed that effects were larger, although not significantly so, in the higher quality group of meta-analyses, all of which were published since 2015.

Conclusions

Journals are full of studies that investigate relationships between psychological variables and sport performance. Since 1983, researchers have utilized meta-analytic methods to summarize these single studies, and the pace is accelerating, with six relevant meta-analyses published since 2020. Unquestionably, sport psychology and performance research is fraught with limitations related to unsophisticated experimental designs. In our aggregation of the effect size values, most were small-to-moderate in meaningfulness with a handful of large values. Whether these moderate and large values could be replicated using more sophisticated research designs is unknown. We encourage use of improved research designs, at the minimum the use of control conditions. Likewise, we encourage researchers to adhere to meta-analytic guidelines such as PRISMA and for journals to insist on such adherence as a prerequisite for the acceptance of reviews. Although such guidelines can appear as a ‘painting by numbers’ approach, while reviewing the meta-analyses, we encountered difficulty in assessing and finding pertinent information for our study characteristics and quality ratings. In conclusion, much research exists in the form of quantitative reviews of studies published since 1934, almost 100 years after the very first publication about sport psychology and performance [ 2 ]. Sport psychology is now truly global in terms of academic pursuits and professional practice and the need for best practice information plus a strong evidence base for the efficacy of interventions is paramount. We should strive as a profession to research and provide best practices to athletes and the general community of those seeking performance improvements.

Supporting information

S1 checklist..

https://doi.org/10.1371/journal.pone.0263408.s001

Acknowledgments

We acknowledge the work of all academics since Koch in 1830 [ 2 ] for their efforts to research and promote the practice of applied sport psychology.

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• 31 March 2021

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The competition to be crowned the fastest, strongest or most technically proficient sportsperson on the planet will once again reach its peak this summer when athletes descend on Tokyo for the Olympic Games. The global pandemic might rule out the throng of enthusiastic spectators that are typical of such an event, but millions will eagerly watch on television as the very best go toe-to-toe.

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A survey of competitive sports data visualization and visual analysis

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• Volume 24 , pages 47–67, ( 2021 )

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• Meng Du   ORCID: orcid.org/0000-0002-6050-2737 2 , 3   nAff1 &
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Competitive sports data visualization is an increasingly important research direction in the field of information visualization. It is also an important basis for studying human behavioral pattern and activity habits. In this paper, we provide a taxonomy of sports data visualization and summarize the state-of-the-art research from four aspects of data types, main tasks and visualization techniques and visual analysis. Specifically, we first put sports data into two categories: spatiotemporal information and statistical information. Then, we propose three main tasks for competitive sports data visualization: feature presentation, feature comparison and feature prediction. Furthermore, we classify competitive sports data visualization techniques based on data characteristics into five categories: high-dimensional data visualization, time-series visualization, graph (network) visualization, glyph visualization and other visualization, and we analyze the relationship between major tasks and visualization techniques. We also introduce visual analysis research work of competitive sports, propose the features and limitations of competitive sports data, summarize multimedia visualization in competitive sports and finally discuss visual analysis evaluation. In this survey, we attempt to help readers to find appropriate techniques for different data types and different tasks. Our paper also intends to provide guidelines and references for future researchers when they study human behavior and moving patterns.

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Acknowledgements

This work is supported by the National Key Research and Development Program of China (2016QY02D0304). We appreciate all the authors who gave us permission to reuse their images in this research. We would also like to thank all researchers for their contributions in competitive sports visualization field and the editors of this journal and the anonymous reviewers for their valuable suggestions and comments.

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National Engineering Laboratory for Big Data Analysis and Application and Beijing Engineering Technology Research Center of Virtual Simulation and Visualization, Peking University, Beijing, China

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Du, M., Yuan, X. A survey of competitive sports data visualization and visual analysis. J Vis 24 , 47–67 (2021). https://doi.org/10.1007/s12650-020-00687-2

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An analysis of the geographic distribution of minor league sports teams.

George Minoso 2024-05-01T12:50:45-05:00 May 3rd, 2024 | General , Research , Sports Management |

Authors: Dr. Mark Mitchell 1 , Richard Flight 2 , and Sara Nimmo 3

Corresponding Author:

Mark Mitchell, DBA

Professor of Marketing

Associate Dean, Wall College of Business

NCAA Faculty Athletics Representative (FAR)

Coastal Carolina University

P. O. Box 261954

Conway, SC 29528

[email protected]

(843) 349-2392

1 Mark Mitchell, DBA is Professor of Marketing at Coastal Carolina University in Conway, SC.

2 Richard Flight, PhD is Associate Professor of Marketing at Coastal Carolina University in Conway, SC. He previously worked in minor league baseball with the Memphis Redbirds and Birmingham Barons as well as in DI collegiate athletics at Samford University.

3 Sara Nimmo currently serves as Assistant Director of Marketing for San Diego State University Athletics. She previously served as a Fan Engagement Assistant with MiLB’s Myrtle Beach Pelicans.

Purpose: The purpose of this study is to evaluate the geographic distribution of minor league sports teams in the United States and Canada.

Methods: A census of minor league sports teams was assembled by collecting data from league websites and other sources. Then, the data was sorted by city and state (or Canadian province). This process allowed the identification of the cities and states/provinces that host the largest number of minor league teams and leagues.

Results: Minor league sports teams can be found in 43 of 50 U.S. states (86%) and the District of Columbia (i.e., Washington, DC) and 8 of 10 (80%) Canadian provinces. There are 12 North American cities or metropolitan areas that host four or more minor league teams: Atlanta, GA; Austin, TX; Birmingham, AL; Dallas-Fort Worth, TX; Des Moines, IA; Las Vegas, NV; New York, NY; Oklahoma City, OK; Salt Lake City, UT; San Antonio, TX; San Jose, CA; and Toronto, Ontario. Additionally, there are 24 cities that host three minor league teams that are distributed across 20 different states and provinces.

Conclusions: While select cities have attracted multiple minor league teams to their communities, these teams tend to be dispersed all over the United States and Canada. As expected, states with larger populations tend to host more teams. States with weather that allows year-round outdoor play tend to host more teams. Cities with successful franchises can use that demonstrated fan support to attract new teams and leagues to their communities.

Applications in Sport: In addition to offering family entertainment, the minor leagues offer both players and professional staff the opportunity to enter the business of professional sports and work toward careers at the major league level. The results of this study illustrate where minor league teams can be found in the United States and Canada. From this list of cities, sports fans can watch up-and-coming players develop. Furthermore, sport educators can direct their students (i.e., aspiring sport administrators) to the cities and teams that may provide them with an entry-point into the field of sports administration.

Key Words: Minor league sports, sports expansion possibilities, minor league team affiliations

INTRODUCTION

Organized sports may be thought of as the games people play. However, there is a very large business and financial infrastructure behind the scenes to allow those games to be played and the related fan experiences to be realized. Plunket Research estimated the total U.S. sports and recreation industry to be valued at over $550 billion in 2020 with the global market estimated to be worth $1.5 trillion (28).

Players making it to the major league of their sport have had to successfully navigate a developmental path by playing in the minor league system and earning successive promotions to earn a spot on a major league roster. In some cases, such as baseball, basketball, and hockey, these minor league teams represent hierarchical levels in a player development path that is clearly laid out. This focus on player development prompted Major League Baseball to restructure its minor league system beginning with the 2021 season. The new model provided for increased player salaries, modernized facilities, and reduced travel time and costs. The restructuring reduced the number of affiliated teams from 160 to 120 (12, 20).

Many colleges and universities offer sport management programs to serve interested students. Currently, there are 421 sport management programs in the United States at the Associates, Bachelors, Masters, and Doctoral levels (33). At the undergraduate level, Sport Management is the 38th most popular major among students. Each year, over 11,000 bachelor’s degrees in sport management are awarded (10). Furthermore, students from other disciplines (e.g., business, physical therapy, nutrition, hospitality, and others) often seek to apply their skills in the business and operation of sports teams. Much like athletes who seek to secure a position in the minor leagues to begin their hopeful path to the major leagues, many people interested in careers in sports administration and sports management begin their careers in the minor leagues as well.

The purpose of this study is to conduct an analysis of the geographic distribution of minor league sports teams and leagues in the United States and Canada. The results of this study will illustrate the cities, states, and provinces that currently host the most minor league teams. From this data, sports fans can incorporate a minor league game into their travel plans while prospective employees can see where their opportunities may be found and focus their job search activities accordingly. First, a broad overview of major and minor league sports is provided, including a look at the possible affiliations between major and minor league teams. Second, the geographic distribution of minor league teams will be provided to illustrate those states and cities that host multiple teams. Finally, the matrices of major and minor league cities are examined to identify the communities most likely to be discussed as expansion cities for major league sports.

THE ORGANIZATION OF MAJOR LEAGUE AND MINOR LEAGUE SPORTS  

In the sections that follow, the teams and leagues involved in the major spectator team sports are profiled. Sports that have a longer professional history (such as football, baseball, or basketball) have a clear path of player development and a delineation between their ‘major’ and ‘minor’ leagues. For these sports, the minor league teams are included in this study.

Other newer professional leagues (such as women’s soccer, women’s ice hockey, or men’s lacrosse), have not yet established a hierarchical path for player development. Rather, it is evolving and, in some cases, changing annually. As such, the athletes who do progress to compete at the highest available professional level (i.e., NWSL, PWHL, or NLL) do realize a pinnacle or ‘major’ achievement. However, these teams and leagues are more similar operationally (attendance, budgets, etc.) to minor league sports rather than the traditional major league sports of football, baseball, or basketball. For these sports, these teams and leagues are included in this study. In the future, with the stability and expansion of these leagues, these sports may attain the classification of ‘major’ league sports.

Men’s Baseball

There are currently 30 Major League Baseball (MLB) teams operating in the United States and Canada (18). Each of these teams has an affiliated Triple-A, Double-A, High-A, and Low-A team. Additionally, MLB operates two leagues for first-year players: Arizona Complex League (ACL) and the Florida Complex League (FCL) where games are played at the Spring Training sites of MLB teams. Additional teams bring the total to 179 teams across 17 leagues in 43 states and 4 provinces (20). A list of minor league baseball teams is provided in Appendix A.

Appendix A: Major League Baseball and Minor League Affiliates  

Source: (20).  

Men’s Basketball

There are currently 30 National Basketball Association (NBA) teams playing in the United States and Canada; 28 of these teams have an affiliated G-League (or, minor league) team (27). Two teams (G League Ignite of Las Vegas, NV; Capitanes Ciudad De Mexico of Mexico City) operate independently and without NBA team affiliation (1). A profile of NBA G-League teams is provided in Appendix B.

Appendix B: G-League Teams and NBA Affiliations  

Source: (27). 

Women’s Basketball

There are currently 12 Women’s National Basketball Association (WNBA) teams playing in the United States (40). There is no existing minor league development system for the WNBA. With just 12 teams and a maximum of 12 roster spots per team (compared to 15 roster spots for the NBA), the competition for one of these coveted roster spots is intense. Players selected in the three-round draft are not guaranteed a roster spot. There has not been any recent expansion of the WNBA despite calls to expand opportunities for women athletes (39).

Men’s Hockey

There are currently 32 National Hockey League (NHL) teams playing in the United States and Canada (24). The American Hockey League (AHL) serves as the top development league for the NHL. There are currently 32 AHL teams playing in the United States and Canada (6). The vast majority of AHL players were selected in the NHL draft and have been signed to player development contracts (17). A level below the AHL is the ECHL (formerly known as the East Coast Hockey League) with 28 teams, with each team affiliated with an AHL and NHL team (11). A list of AHL and ECHL teams is provided in Appendix C.

Appendix C: American Hockey League Teams and Affiliated NHL Teams  

Source: (13). 

Men’s Soccer

There are currently 29 Major League Soccer (MLS) teams playing in the United States and Canada (19). The USL Championship League is sanctioned by the U.S. Soccer Federation as a Division II professional league. The USL Championship League includes 24 teams located in the United States with expansion teams planned. A level below, the USL League One has 12 teams with 2 expansion teams planned. (36). A list of USL Championship and USL League One teams is provided in Appendix D.

Source: (36). 

Women’s Soccer

There are currently 14 National Women’s Soccer League (NWSL) teams competing in the United States (26). A list of NWSL teams is provided in Appendix E. The United Soccer League (USL) is introducing the USL W League in Summer 2024. There are plans for 44 teams located in 20 different states. The USL W League hopes to “bring elite women’s soccer to communities across the U.S., creating more opportunities to play, watch and work in the women’s game.” The USL W league will be introduced as a para-professional league, meaning the players will retain their amateur status (37). For this reason, these teams are not included in this analysis.

Men’s Football

There are currently 32 National Football League (NFL) teams competing in the United States (23) and 9 Canadian Football League (CFL) teams competing in Canada (9). Over time, there have been competing and/or feeder leagues to the NFL, including the World Football League (WFL), the United States Football League (USFL), the Extreme Football League (XFL), and the Spring League. In December 2023, it was announced that the USFL and XFL would merge to create the United Football League (UFL) and begin play in the spring of 2024 (32). Through the merger process, eight teams were retained and eight teams ceased operations. One city (Houston, TX) previously hosted both USFL and XFL teams prior to the merger. The XFL Houston Roughnecks ‘survived’ the merger while the USFL Houston Gamblers did not. The following cities lost their USFL and XFL teams beginning in the 2024 season (16):

New York/New Jersey Metro

New Orleans, LA

Philadelphia, PA

Pittsburgh, PA

Orlando, FL

Seattle, WA

Las Vegas, NA

Indoor or Arena Football has been played in various locations since the mid-1980s with the Indoor Football League (IFL) being the longest-running league. There are 16 IFL teams playing in 2024. IFL personnel, including players, coaches, scouts and front office professionals have transitioned to the National Football League (15). In addition, the National Arena League (NAL) operates a 6-team league (22). A review of the various non-NFL football teams is provided in Appendix F.

Men’s Lacrosse

There are currently 15 National Lacrosse League (NLL) teams competing in the United States and Canada (25). The league plays its games in indoor arenas, often the same arenas that host minor league hockey and NBA G-League basketball teams. A list of NLL teams is provided in Appendix G. Beginning in Summer 2023, the Premier Lacrosse League started play with 8 teams in the United States. In its inaugural season, all 8 teams travelled to a select city for competition each weekend. City names are not attached to teams (29). As such, these teams are not included in this analysis.

Women’s Professional Hockey

The Professional Women’s Hockey League (PWHL) began its inaugural season in January 2024. The newly-created league consists of 6 teams across the United States and Canada with teams located in Boston, Minneapolis, Montreal, New York City, Ottawa, and Toronto (30).

Miscellaneous: Athletes United

Since 2020, Athletes Unlimited has introduced professional leagues in women’s basketball, volleyball, lacrosse, and softball. The leagues state they are ‘player-centric’ while avoiding the traditional model of city-identified teams. With this model, many American athletes can play professionally in their home country rather than competing abroad (7). However, teams are not based in home cities. As such, these teams are not included in this analysis.

METHODOLOGY  

The minor league teams and leagues profiled above that operated in the 2023-24 seasons were identified and assembled into a database to allow the analysis of the location of the teams. The sorting function in Microsoft Excel allowed the researchers to identify the frequency of occurrence for city, state, and province, resulting in the identification of the following groups: 

• States and/or provinces that host the most minor league teams; 
• Cities that host the most minor league teams; 
• Cities that are most likely to be considered for league expansion in the future. 

While select cities have attracted multiple minor league sports teams to their communities, these teams tend to be dispersed all over the United States and Canada. In the United States, 43 of 50 states (86%) host at least one minor league team. The states that do not current host a team are Alaska, Hawaii, Louisiana, Montana, North Dakota, Vermont, and Wyoming. In the Lower 48 states (excluding Alaska and Hawaii), minor league sports can be found in 43 of 48 (90%) of the states with the missing states being sparsely populated (with the notable exception of Louisiana).

In Canada, minor league teams can be found in 8 of 13 Canadian Provinces or Territories. The provinces that do not current host a team are New Brunswick, Northwest Territories, Nunavut, Prince Edward Island, and Yukon. Similar to the pattern found in the United States, teams can be found in 8 of 10 Canadian provinces (80%) with no teams located in the three more sparsely-populated Canadian Territories of Northwest, Nunavut, and the Yukon.

A city-by-city mapping of each minor league team located in the United States and Canada is presented in Figure 1. The heat mapping function in Microsoft Excel was used to generate Figure 2, a visual presentation of the frequency of location of minor league teams per state and province.

Interestingly, minor league teams have been located previously in Hawaii (baseball), Louisiana (baseball), Montana (baseball), North Dakota (indoor football), Vermont (baseball), and Wyoming (baseball). However, no teams existed in these states during the 2023-24 season. In fact, some of these baseball teams were among the 40 teams affected by the realignment of minor league baseball to begin the 2021 season (see 20, 31).

State-by-State Analysis

The following states host the largest number of minor league teams:

California (26 teams in 17 different communities)

Texas (25 teams in 15 different communities)

Florida (23 teams in 16 different communities)

New York (19 teams in 12 different communities)

North Carolina (17 teams in 12 different communities)

Pennsylvania (12 teams in 9 different communities)

Ohio (10 teams in 7 different communities)

Georgia (9 teams in 8 different communities)

Iowa (8 teams in 5 different communities)

Michigan (8 teams in 5 different communities)

South Carolina (8 teams in 4 different communities)

Oklahoma (7 teams I 2 different communities)

Washington (7 teams in 4 different communities)

Arizona (7 teams in 3 different communities)

Indiana (7 teams in 3 different communities)

Virginia (7 teams in 5 different communities)

Province-by Province Analysis 

The following Canadian provinces host the largest number of minor league teams:

Ontario (6 teams in 3 communities)

British Columbia (3 teams in 2 communities)

Quebec (3 teams in 2 communities)

Alberta, Manitoba, Newfoundland and Labrador, Nova Scotia, and Saskatoon (1 team each)

It must be noted that junior hockey is a very popular spectator sport in Canada. However, most junior hockey players are classified as ‘amateurs’ (2). For this reason, Canadian junior hockey teams are not included in this analysis.

City-by-City Analysis 

As illustrated above, many communities host more than one minor league team. Furthermore, some cities with minor league teams also host major league sports teams. For example, Charlotte, North Carolina hosts an NFL team (Carolina Panthers), an NBA team (Charlotte Hornets), and an MLS team (Charlotte FC) in addition to hosting minor league teams in baseball, hockey, and soccer. Nearby Greensboro, North Carolina also hosts three minor league teams in basketball, indoor football, and baseball but hosts no major league teams.

Table 1 provides an overview of the 12 cities that host four or more minor league teams. The reader will note that some the cities are larger metropolitan areas with teams located both in the city and the suburbs. Atlanta, for example, has one team in the city but four teams in its suburbs in close proximity to central Atlanta. These communities with a concentration of minor league teams often host additional sporting events, such as golf tournaments, auto races, or college football bowl games.

San Diego is an interesting case. In addition to hosting the San Diego Padres (MLB), the city previously hosted an NFL team (San Diego Chargers) and two NBA teams (San Diego Rockets and San Diego Clippers). All three of these professional teams continue to exist but relocated to other cities. San Diego has effectively attracted minor league teams to fill the voids left by the departure of these teams. Recently, the San Diego Loyal soccer team (USL Championship League) ceased operations after the 2023 season after failing to find a long-term home stadium option (14). However, an MLS expansion team (to be known as San Diego FC) will begin play in the 2025 season (34).

Table 2 provides an overview of cities that host three minor league teams. Included in Table 2 is each city’s ranking in size as a media market (21). Also, any professional teams in these same cities are shown with their table cell shaded. Sports not currently playing in those communities represent opportunities to expand a city’s minor league sports portfolio. It is interesting to note that some of these 3-team cities (such as Worchester, MA or Tacoma, WA) are very close to neighboring cities of top 15 media markets.

DISCUSSION  

As expected, larger states with larger populations tend to host more minor league teams. Concurrently, cities with larger populations (and larger media markets) tend to host more minor league teams. The three states with largest number of minor league teams (California, Texas, and Florida) also offer a climate conducive to year-round outdoor activities. Cities with successful franchises can use that demonstration of fan support to attract new teams and leagues to their communities. Furthermore, shared facilities (such as an arena that can host basketball, hockey, and arena football) can help bring new teams to a community.

As previously noted, many cities host both major and minor league teams. Intuitively, these locations should attract the most attention should leagues consider expansion as the fan bases have demonstrated sufficient levels of support to sustain a major league team. These cities are listed in Table 3. Additionally, these cities tend to be the larger media markets with larger numbers of consumers. As an illustration, at the time of this writing the Oakland Athletics are strongly considering moving to Las Vegas, NV and have already received the approval to move by Major League Baseball owners (3-5).

INSERT TBL3

A Cautionary Note – Minor League Baseball Relocations  

In 2020, Major League Baseball issued new facility standards for minor league teams, including: minimum clubhouse sizes for both home and visiting teams; food preparation and dining areas attached to clubhouses; better field lighting; more and better training space for players; separate space for female staffer, and others (31). Given that many minor league stadiums are municipally-owned, some communities may be unwilling or unable to make the needed investments in upgrades and may see their teams migrate to other communities, particularly at the A- and AA-levels.

In fact, some team movement has already been announced as the Kinston, North Carolina team (now known as the Down East Wood Ducks) have been purchased by Diamond Baseball Holdings (the largest owner of minor league baseball franchises) and will relocate to a new yet-to-be-built stadium in Spartanburg, South Carolina and assume a new team name as early as the 2025 season (8). This move marks the return of minor league baseball to Spartanburg, which previously hosted the Spartanburg Phillies from 1963-1980 and again from 1986-1994 (38).

CONCLUSIONS

Minor league sports teams are widely distributed across the United States and Canada with 86% of U.S. states and 80% of Canadian provinces hosting at least one minor league team. These 43 U.S. states host 97% of the U.S. population while the 8 provinces host 96% of the Canadian population. The highest concentration of teams can be found in four geographic areas in the United States: (1) the southeast Atlantic corridor from Virginia south through Florida; (2) the eastern Midwest and Northeast including Pennsylvania, New York, and Massachusetts; (3) the Southwest including Texas and its border states; and (4) the West coast primarily concentrated in California. In Canada, Ontario (i.e., the Toronto area), British Columbia (i.e., the Vancouver area), and Quebec host more minor league teams than the other provinces.

In addition to offering family entertainment, the minor leagues offer both players and professional staff the opportunity to enter the business of professional sports and work toward careers at the major league level. The results of this study illustrate where minor league sports teams can be found in the United States and Canada. From this list of cities, sports fans can watch up-and-coming players develop. Furthermore, sport educators can direct their students (i.e., aspiring sport administrators) to teams for internships and entry-level employment opportunities.

APPLICATION IN SPORT

In team sports, most professional athletes go through a player development process that includes some stint in the minor leagues in the hopes of earning a spot on a major league team. Similarly, many sport administrators begin their careers working for minor leagues and affiliated teams as they learn their craft and assemble the needed experiences for (hopeful) promotion to the major league level. The results of this study allow interested parties to easily identify the communities with greater access to minor league sports (for both fans and prospective employees). Sports fans should find this information helpful as minor league sports provide a good financial value in family entertainment. College students may find internship and employment opportunities with these minor league teams to aid their entry into a career of sport administration and management. Sport administration educators may find this information helpful as they advise and counsel their students for internships, co-operative employment opportunities, and job placement after graduation.

The communities identified here with multiple sports properties may allow a student to work in multiple sports in the same city (say, basketball in winter and baseball in spring, summer, and fall). In many instances, there may be an overlap in the ownership groups of minor league teams. This overlap in ownership may expand professional opportunities for employees as well-performing employees are offered additional positions and responsibilities elsewhere in the organization.

These communities also tend to host other events, such as college football bowl games or golf tournaments. These special events will need qualified staff to deliver these events, which will include people already living and working in those communities in the sports industry. Much like athletes in the minor leagues work to advance toward the major league ranks, so, too, can staff personnel ‘climb the ladder’ toward careers in the major leagues.

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Male Competitive Powerlifters relationship with Body Image: Utilising the Multidimensional Body Image Self Relations Questionnaire (MBSRQ)

George Minoso 2024-04-22T08:06:50-05:00 April 20th, 2024 | General , Research , Sport Training , Sports Exercise Science |

Authors: Dr. Mark Chen 1 , Dr. Andrew Richardson 2

1School of Health and Life Sciences, Teesside University, UK (corresponding author) 2 Population and Health Sciences Unit, Newcastle University UK

Corresponding Author: Mark Chen Campus Heart, Southfield Road, Middlesbrough TS1 3BX, Tees Valley [email protected]

Dr Mark Chen is a Senior Lecturer in Sport and Exercise Science at Teesside University and is a Chartered Psychologist with the British Psychological Society (BPS). Dr Chen’s research interests include psychological consequences of sports injury and attentional aspects of sports performance.

Dr. Andrew Richardson is a Chartered Heath and Activity Practitioner with the Chartered Institute for the Management of Sport and Physical Activity (CIMSPA) and is currently a Research Associate within the Population and Health Sciences Institute at Newcastle University. Andrew’s other research interests include body image, performance enhancing drugs, transgender sport, esports and public health..

Male Competitive Powerlifters relationship with Body Image: Utilising the Multidimensional Body Image Self Relations Questionnaire (MBSRQ).

Purpose: There is growing evidence to suggest that competitive male athletes in aesthetic sports that scrutinize their body image may experience undesirable mental health outcomes. However, there is limited research to address these issues in strength sports, particularly the sport of Powerlifting. Methods: This study employed the Multidimensional Body Image Self Relations Questionnaire (MBSRQ), which recruited 365 male participants across the following subgroups. Powerlifters (P) (n = 133), Active Subjects (AS) (n = 79), Appearance Based Sports (ABS) (n = 68), Strength Sports (SS) (n = 47) and Other Sports (OS) (n = 38). Results: One–way ANOVA showed significant (p < 0.05) results between all groups across six of the nine MBSRQ subscales. Post hoc comparisons found nine significant results with the powerlifting group achieving two of them against OS (p < 0.01) and AS (p < 0.01) groups respectively. Conclusions: Overall, the results showed that male powerlifters expressed their bodies-as-function rather than their bodies-as-object with regard to health evaluation and fitness orientation. This is supported by their stable and balanced scores across the MBSRQ subscales which indicates they have healthier and lower perceptions of negative body image concerns. The powerlifters results implied that a focus on objective performance improvement and maintaining a healthy body to prevent injury had body image benefits. Applications in Sport: The study concludes that male powerlifters present healthy body image perceptions compared to the other males in their respective sports and focus on their body functionality objectively rather than the subjective perception and presentation of their body image.

Keywords: Powerlifting, Body Image, Weight Classed Sports

For this paper, the definition of Body image is referred to as “relating to a person’s perceptions, feelings and thoughts about his or her body, and is usually conceptualized as incorporating body size estimation, evaluation of body attractiveness and emotions associated with body shape and size” [1-2]. There has been extensive work conducted on the influence of body image in the media [3], in Western culture [4] and job roles such as the fitness industry [5]. Other comparisons include comparing body image within a range of demographic factors such as between athletes and non-athletes [6], age [7], nationality and ethnicity [8]. Cash and Pruzinsky [9] have defined five dimensions of body image, which work together to create an overall body image. However, these dimensions fails to mention the broader cultural and social contexts that influence body image [10]. They suggested that athletes dealing with sporting and societal pressures may experience adverse outcomes such as eating disorders or a negative perception of their body image. Such factors may lead to these pressures as a result of media and advertisements [11], supplements [12] and the use of image and performance-enhancing drugs [13].

Background of Powerlifting

Powerlifting athletes are scored on objective performance measures rather than appearance evaluations. Powerlifting tests athletes on their objective strength and has traditionally been male-dominated [14]. However, in the last twenty years, female participation has significantly increased [15]. Richardson and Chen [16] state that powerlifting is a competitive strength sport comprising three techniques: the Squat, the Bench Press and the Deadlift [17-18]. The aim is to lift the most weight across the three movements for nine attempts [18]. Sports similar to powerlifting that heavily rely upon strength include Olympic weightlifting [19], strongman [20], highland games [21] and the shot–put [22], to name but a few examples. Not all of these sports mentioned have a weight class or a weight requirement, but for those that do, depending on the rules of the competition, this weight requirement may be evaluated within twenty-four or even forty-eight hours prior to the event [23]. Weight classes help ensure fairness in competition and increase the pre-competition demands of participants to enter the weight category that maximizes their advantages. Experts argue that making weight places psychological demands on athletes who may be inclined to make drastic weight cuts to gain a competitive advantage [24]. However, as powerlifters are evaluated on the amount of weight lifted, the training is based on objective scoring criteria. As scoring is objectively determined, and not a third party as in aesthetic sports, this has important implications for positive psychological adaptations [25].

Theoretical models and frameworks

Theoretical models of body image, such as Objectification theory, focus on the impact on men of a culture that increasingly objectifies men’s bodies. It suggests that men, like women, may experience self-objectification [26]. For men, the dual focus on both leanness and muscularity characterizing the male body ideal may motivate a particularly maladaptive set of behaviors designed to achieve these goals, such as rigid exercise routines, hidden use of image and performance-enhancing drugs (IPEDs) [27]. Subsequently, the literature has claimed that men may suffer from body image concerns and dysfunctional behavior [28]. Some research argues that young men experience societal pressure to achieve the muscular mesomorphic body shape, and this behaviour leads to a drive for muscularity [29].

Further, studies have demonstrated that sociocultural pressures mediated by social comparisons and internalization of muscular and low-fat ideals are associated with men’s body dissatisfaction and drive for muscularity, which might lead to disordered eating [30]. Most research has focused on aesthetic sports such as bodybuilding [31-32]. These explanations fail to consider how individuals think, feel and behave concerning their body functionality [33]. How powerlifters think, feel, and behave about their body functionality in a sport concerned with achieving objective demands is essential to achieving a more complete and holistic understanding of body image in this context [34].

Theoretically, the subjective perception of muscularity depends on the individuals’ perception of body image, which for powerlifting tends toward a functional muscularity rather than aesthetic muscularity due to the sport’s rules. Critically, the self-objectification model does not consider the functionally orientated nature of sporting competition and its impact on male psychology [35]. Therefore, the athletes have a strong sense of control and need to prepare, train and diet concerning maximizing objective performance criteria, not gaining approval from judges based on aesthetics. The environmental demand to achieve an objective standard has essential implications for broadening body image, as Ginis et al., [36] reported. They found that the idea of muscularity and physical competence in men [37] are central to their evaluations of their bodies. According to Conceptualisation theory, men are socialized to focus more attention on their body functionality than body-as-object (image) [38]. Therefore, powerlifting males are likely to value the functionality of their body over appearance, not only due to socialization processes that favour the achievement of tangible performance-based outcomes [39-40] but also due to the specific environmental demands of powerlifting which reward objective performance results. In contrast, perceptions of leanness and body fat percentage are less relevant to powerlifters performance. Franzoi [38] defined body-as-process as comprising physical capabilities and internal processes, which is similar to body functionality. The demand for functionality adds sources of experience, such as training to execute specific external and internal demands, that requires knowledge of body functionality (movement) and is, therefore, adaptive for how male powerlifters individuals think and feel about their body image [38].

For example, Richardson and Chen [16] found that female powerlifters, despite presumably having been socialized to experience higher levels of self-objectification and greater body-as-object identification than men, as predicted by self-objectification theory, nevertheless enjoyed their appearance in their sporting environment, indicating that it was not a source of anxiety, presumably due to the enjoyable experience of functional powerlifting training and competition reward. This was evident in other studies using smaller sample sizes and qualitative interviews in the same sport and sex [14 & 41]. Bordo [42] found that individuals who presented with large muscular physiques symbolized strength and masculinity.

Competition achievement and social reward within a sport based on tangible athletic goals [43-44] and psychological characteristics such as aggression when preparing to lift [45] will strongly mitigate against excessive rumination around body appearance and identity. Further reasoning supports the powerlifting community’s emphasis on body functionality [46-47]. From this perspective, male powerlifters likely develop a functional appreciation of their body that is valued separately from its appearance. This construct of functionality appreciation has only recently been investigated in the context of positive body image. It is positively associated with positive body image facets, such as body appreciation [48].

Franzoi [38] proposed that individuals hold more positive attitudes toward their body functionality than their body image. Therefore, it can be predicted that males with this orientation will hold performance adaptive attitudes toward their bodies. Body conceptualization theory offers a rationale for the body functionality being adaptive and reflective of positive male body image and improved mental health, compared to a body image orientation. This theorizing gives scope that negative body image attitudes can be adaptive and motivational within a performance-based environment based on objective rather than subjective and image-based criteria. For the male powerlifters, this would be the performance their bodies execute to meet the environmental needs (e.g., the sporting demands of their event). For example, Gattario and Frisen [49] found that males stated that finding a social context in which they found belonging and acceptance that allowed them to develop agency and empowerment allowed them to move from a negative to positive body image. With this logic, it could be predicted that competitive powerlifters will differ in their positive body image compared to individuals who are active but don’t compete.

Nevertheless, functionality measures have focused predominantly on physical capacities and internal processes and have typically concerned physical strength and muscularity. These aspects of body functionality can be conflated with physical appearance and are accentuated by male appearance ideals and the male gender role emphasizing dominance, power, and strength [50-51]. There has been some research into the body image perceptions of athletes in strength sports. Goltz et al [52] divided 156 male athletes into weight-class sports, endurance sports and aesthetic criteria sports and found no differences in body shape concerning self-depreciation due to physical appearance. Richardson and Chen [16] found no association between negative perceptions of appearance for female powerlifters compared to aesthetic sports individuals. These results suggested that the powerlifting group had contentment with their appearance, perhaps due to the decreased emphasis on body image compared to the increased emphasis on body functionality and focus on improving their skills and strength for their sport.

Apart from these few studies, research has yet to be done on body image and functionality in male powerlifting. The association of the physical body with functional sporting competition achievement based on objective standards may reduce the potential for internalizing negative body image and lead to healthy adaptations based on physical demands. This research will explore what functionality means for male powerlifters and how this impacts body image and functionality. This study aims to compare the body image of male powerlifting athletes against other subgroups of male athletic participation. The aim is to examine if male powerlifting athletes express different body image satisfaction or dissatisfaction with their body image and weight compared to subgroups of active and or sporting males.

Aim and Objectives of the Study 

To compare the body image differences of male powerlifters against a range of male athletic subgroups. 

● The first objective was to determine if the powerlifters have significantly lower scores regarding their bodyweight perception when compared to other male groups in the study.

● To determine if powerlifters present an emphasis on body-as-process rather than body-as-object.

Participant Information

An opportunity sample of 365 males was recruited through Facebook and Instagram. The recruitment period lasted three weeks in length and generated the following subgroups. There were 133 Powerlifters (P), 79 Active Subjects (AS), 68 Appearance Based Sports (ABS) participants, 47 Strength Sports (SS) participants and finally, 38 Other Sports (OS) participants within their respective subgroups. The group sample means and standard deviations for their age were 28.65 (± 7.44), height was 178.58cm (± 13.3cm), and their weight was recorded at 89.99kg (± 18.20kg). 

Within Table 1.0, each subgroup’s means and standard deviations were recorded for their age, height, weight and the length of time they have spent training. The powerlifting (P) group mean age was 27.71 ± 6.86 years, the mean weight was 92.73kg ± 21.24kg, and the mean height was 176.67 ± 15.27cm. Appearance Based Sports (ABS) group mean age was 28.04 ± 7.59 years, mean weight was 86.89 ± 14.55kg, and height was 177.11 ± 12.32cm. The active Subjects (AS) group’s mean age was 30.30 ± 8.19 years, the mean weight was 84.99 ± 12.81kg, and the mean height was 179.85 ± 14.91cm. The strength Sports (SS) group’s mean age was 29.19 ± 7.26 years, the mean weight was 97.41 ± 20.11kg, and the mean height was 181.69 ± 7.02cm. In the final subgroup Other Sports (OS) group, the mean age was 28.95± 7.49 years, the mean weight was 87.19 ± 15.53kg, and the mean height was 181.47 ± 7.87cm. No ethnic identity data was recorded. The study was conducted after obtaining ethical approval from the Teesside University School of Social Science Business and Law Ethical Approvals Committee. 

Measures 

Multidimensional Body Self Relations Questionnaire (MBSRQ): The MBSQR measures Body Image divided into cognitive and behavioral components [53]. Items are ranked on a 1 to 5 Likert scale, where 1 = Definitely disagree, and 5 = Definitely agree. The MSBRQ subscales include Appearance Evaluation (AE), Appearance Orientation (AO), Fitness Evaluation (FE), Fitness Orientation (FO), Health Evaluation (HE), Health Orientation (HO), Illness Orientation (IO), Body Areas Satisfaction (BASS), Overweight Preoccupation (OWP) and Self-Classified Weight (SCW). Illness Orientation is not included as a separate subscale, as it is already reliably accounted for under Health Orientation. The MBSRQ is significantly evidenced in Body Image research [9 & 53] as a well-validated measure [54] through comparison with other tools of Body Image. The MBSRQ has a proven reliability and validity record for body image research [53]. The composite reliability was calculated using an SPSS Omega Macro [55] and is within the acceptable range (Cronbach’s omega = 0.79). The primary author constructed demographic questions to collect information about the participant’ background. These questions included (but were not limited to) sex, age, height, weight, and years spent training. 

Both the MSBRQ and Demographic Questionnaire were developed using Google Documents. Data gathered was stored under the General Data Protection Act [56]. Participants were assigned to groups 1.00 (Powerlifters – P), 2.00 (Appearance Based Sports – ABS), 3.00 (Active Subjects – AS), 4.00 (Strength Sports – SS) and 5.00 (Other Sports – OS), based on their answers from the demographic questionnaire. Participants were given no monetary or external incentive to take part. Participants read the pre-questionnaire information, participant information form and questionnaire instructions. Once read, participants were prompted to check a box that confirmed their consent to the study. All participants completed the questionnaire individually and received no communication from the researcher during data entry. A glossary was provided for technical terminology. Demographic questions were formatted as short answers, rating scales, and multiple-choice. Participants were informed they could opt out anytime during the study for any reason. In total, the questionnaires took about 10-15 minutes to complete.

Data Analysis

An independent group design was used to investigate the differences between the MBSRQ scores of the four. The dependent variables measured the differences in body image between the groups across nine subscales. All data were analyzed using Microsoft Excel version 2016 and Statistical Package for Social Science (SPSS) Version 27. Means and Standard Deviations were calculated for all the subscales. Data were checked for equality of variance between groups and assumptions for the one–way ANOVA where the alpha value was set at 0.05. Post hoc tests were calculated to compare the powerlifting group with the other three groups across the MBSRQ subscales. The post hoc alpha values were corrected for type one error rates using p < 0.01. To estimate the effect size of post hoc mean differences between groups, the Cohens d statistic size was interpreted using the following guidelines: .00-.2 (small), .40-.79, (medium) and .80 + (Large) [57] and 95% Confidence Intervals (CI) were reported. The Hedges g statistic was used if one or both groups being compared had n < 20, otherwise, Cohens d was reported.

The descriptive statistics associated with the MBSRQ scores across the five groups are reported in Table 2.0. It can be observed that the powerlifting group was associated with higher, consistently stable and healthy body image scores in comparison to the other four male sub-groups. Six of the nine MBSRQ subscales reported p-values below 0.05.

Below are the graphs of the nine subscales from the MBSRQ presented to showcase the differences in mean scores for each domain of body image.

DISCUSSION This study aimed to compare the body image of male powerlifters with sporting and physically active males. There were multiple significant results across six of the nine MBSRQ subscales between the groups. Overall, the results of this study suggest that male powerlifters have a healthy relationship with their physical body when compared to all other groups. The powerlifters on average, evaluated both their health and fitness orientation were higher compared to both physically active males and males in other sports. Comparing the groups anthropometrics, all groups expressed similar heights, weights and mean age. Most participants from the powerlifting group were in the late twenties, average weight at 92.73kg and standing around 178cm in height. Nolan, Lynch and Egan [58] used a male sample that was comparable to the current study in size and age. Other studies recruiting male powerlifters all had smaller sample sizes and younger age ranges [59-60] compared to the current study.

The first objective was to determine if the powerlifters had significantly lower scores regarding their bodyweight perception when compared to other male groups in the study. There was no evidence to support this prediction, as the powerlifting group levels of overweight preoccupation and self-classified weight area satisfaction were not significantly different from the other groups. The Powerlifting group had scored 2.49 for the OWP subscale which was higher than both SS and OS groups but lower than AS was the powerlifting and ABS groups. This would appear to indicate that the male powerlifters either do not ruminate on their body-as-object to the detriment of their mental health or that the nature of engagement with the powerlifting competitive demands lends itself toward a functional conceptualization of the body over an image-based focus [61]. These results taken together do not imply that powerlifters demonstrated a negative perception of their body image. Rather, the results suggest that powerlifters link their body image toward objective performance related goals. Although, this is speculative, the intense regime of powerlifting training for competition would result to improved perceptions of body image due to perceived changes in strength over time.

Theoretically, powerlifters interpreting their body-as-process rather than the body-as-object is consistent with larger differences in Fitness Orientation, Health Evaluation and Overweight – Preoccupation compared to the sport male and physically active male groups. These subscales relate more to objective performance concerns, such as physical capacity, rather than the subjective interpretation of body image, thus appear to be accentuated by perceptions of power and strength [50-51]. Fitness orientation refers to, “Extent of investment in being physically fit or athletically competent. High scorers value fitness and are actively involved in activities to enhance or maintain their fitness. Low scorers do not value physical fitness and do not regularly incorporate exercise activities into their lifestyle” [53]. Richardson and Chen [16] found their sample of female powerlifters scored the highest out of this subscale when compared to other groups.

Health Evaluation is defined as, “Feelings of physical health and/or the freedom from physical illness. High scorers feel their bodies are in good health. Low scorers feel unhealthy and experience bodily symptoms of illness or vulnerability to illness” [53]. Richardson and Chen [16] found that their sample of female powerlifters scored the highest on this subscale compared to other sporting females.

Overweight preoccupation reflects “fat anxiety, weight vigilance, dieting, and eating restraint.” [53]. Richardson and Chen [16] found, for their powerlifting group, very stable scores around the normative values with little deviation from the mean, therefore indicating that the group were happy and content with their weight for the function of powerlifting. The Powerlifting group had higher OWP compared to the other two groups but not low enough to indicate extreme weight cutting, dieting or weight anxiety, Although, the nature of powerlifting does require some weight monitoring due to the weight classes requirement, the score was not concerning. An individual-by-individual analysis would need to be considered to accurately assess if an athlete is expressing extreme body weight anxiety or concerns.

Certainly, this does contrast with the findings of the Active subjects (AS) group who had a moderate effect size of greater overweight preoccupation (OWP) and self-classified weight (SCW) compared to Other Sports (OS) and Strength Sports (SS). These difference of the control group (AS) adds further weight for the difference between the powerlifters and the other groups body image. The active subjects were composed of individuals who don’t compete in any sport, but their recreational exercising still did not prevent them from having pre-occupation with their physique. Male exercisers can be as pre-occupied with outward appearance as women due to their motivation for muscularity [62] and also as non-athletes they may lack the functional body appreciation that male athletes possess [63].

The second objective was to determine if powerlifters present an emphasis on body-as-process rather than body-as-object. Theoretically, body functionality can be understood in contrast to appearance ideals and gender roles for men, which emphasise the importance of physical strength, prowess, and bodily control [64]. The absence of negative body image perceptions in the males only lends indirect evidence for a higher emphasis on functional cognitions related to objective performance. There were two significant differences between powerlifters with OS and AS in health evaluation and fitness orientation. There was a moderate effect size difference for health evaluation, with the powerlifting group showing more robust health behaviours than the other sports group.

The other sport group was the smallest group (n=31) and consisted of people who recreationally participated in a variety of sports of which Soccer, Cross fit and Athletics were the most numerous. The health cognitions of the powerlifters place an emphasis on being prepared to execute maximum effort in their training and respecting the possibilities and limit of what they can achieve [65]. Compared to sports such as Athletics and Soccer, which place more emphasis on diverse interceptive open skills in a changing environment and / or endurance, Powerlifting requires maximum and intense concentration to prepare for one explosive movement. Therefore, the powerlifters need to have a healthy attitude toward diet, for example, as performance is related to performing at their physical limits but is not essential for skilled footballers. These results contrast with Goltz et al., [52] who found no differences in self-depreciation due to physical appearance in comparing weight-class sports, endurance sports and aesthetic criteria sports.

The powerlifting group also showed stronger fitness orientation compared to the active subjects groups. This may mean that the powerlifters monitoring of their pre-performance health results in stronger fitness evaluations compared to individuals who only exercise and also individuals in sports with less physically explosive demands [65]. This seems to reinforce the first finding, that male powerlifters have a positive rather than negative view of their body image, in terms of the value they place on health and fitness related cognitions to help prepare for competition. The fitness-orientation aspect can be interpreted for body functionality qualities, as this subscale would support behaviours and cognitions conducive to maintaining good physical condition and a positive view of the body [66]. An explanation in terms of body conceptualization theory is that the functionality of powerlifting competition allows the participants to engage in a wider range of strategies to maintain fitness rather than being concerned with aesthetics, compared to individuals who only exercise [49].

Comparing this to the appearance-based sport (ABS) group, they too also undergo intense and regimented training, as competitors will need to ensure they are in the best condition for competition, although still based on aesthetics. However, where the ABS group differ from the powerlifters is a moderate effect size for overweight preoccupation compared to the OS group. There was also a moderate effect size for self-classified weight compared to the strength sports group. These two subscales are more in line with previous findings [67], in that aesthetic sport participants need to put more effort in body monitoring and judgements related to weight loss or gain. In powerlifting, research has shown that to overcome confounding issues that may affect athletic performance, athletes reported that the following factors help relieve or reduce competition day stressors include, the coach, mental attitudes, technical instruction, training partners and social isolation [67]. When comparing between sexes, the results revealed no fundamental difference in these confounding factors by male and female powerlifters [66]. Within both studies, it was noted that there was no mention of body image when competing to be a compounding factor, which supports the current findings. Nevertheless, the powerlifters body image or perception of their own image was not given as an option in their studies so results may have been different if participants had been given an option to select.

The AS group reported two medium effect sizes against the other sports group and strength sports group, which were in the overweight preoccupation and self-classified weight subscales, but the powerlifting group scored a moderate effect size against the AS group in fitness orientation. The reason for this can be linked to multiple variables. Firstly, the AS group participants as stated earlier in this manuscript are not training to improve their performance within a specific sport or event. They are active males who are training but with no sport specific goal in mind. Hence, these individuals may be more critical of themselves when it comes to focusing on their bodyweight. This can be easily demonstrated in the subscale of SCW where the AS group scored the lowest when compared to the OS and SS groups. As individual in these sports may compete at a weight they are comfortable at, this yields them the best performance advantages when in competition.

Notwithstanding, the AS group did score closer to a mean normative value for their OWP subscale and scored higher than both OS and SS groups. The reason may be that higher scores focus more on weight vigilance and weight anxiety. However, the OS and SS groups scoring lower than AS and having low OWP scores indicates that their sports don’t require, or these athletes didn’t express any worry about their weight when competing.

Nevertheless, there is research to suggest that those who train for body image and pursue masculine muscular ideals may be motivated for these appearances through unhealthy means. These include self – blame and or internalised shame as reported by Larison and Pritchard [68] found that men who scored higher on these variables also reported higher levels of eating disorder symptomology. Yet, in the same study, those same men who scored higher for internalised shame also scored higher on the drive to be more muscular. Finally, Swami and Bedford [69] found that men’s drive for muscularity was significantly predicted by neuroticism and their drive for body appreciated was significantly predicted by neuroticism and extroversion when considering BMI and subjective social status as drivers. However, in other studies the opposite findings have been reported. Reina et al., [67] also reported that males in non-aesthetic sports were more dissatisfied with their body image and were 1.5 times more likely to use exercise to lose weight than non-sport participants.

Limitations The MBSRQ is a valid and reliable and well stablished body image assessment tool and is appropriate for out study [53]. Nevertheless, the MBSRQ does not measure disordered eating or specific ideals of muscularity as compared to other aforementioned assessment tools. The powerlifting group in this study as in the female study by Richardson and Chen [16] is centred around one sport and unlike the other groups they are made up of multiple sports. Ultimately, this will have impacted their scores within their groups and comparing between groups. The powerlifting group as a whole had more training experience than the other groups which is reflected in their larger sample size and more stable scores which has to be factored into the analysis.

CONCLUSIONS In summary, the findings report the powerlifters presented with stable and positive outlooks and evaluations of their body image. This highlights a productive relationship with their own body image and their sport of powerlifting as a body-as-function role instead of body-as-object [47]. Comparing the powerlifters with other sport groups showed similar results. The powerlifters presented with significantly (p < 0.05) better scores for HE and FO subscales in the MBSRQ when compared to the AS and OS groups. The majority of the groups displayed stable MBSRQ subscale scores and healthy outlooks on their body image. The study found that powerlifters did not express or display any extreme perceptions of their body image despite them competing within a defined weight category. These results also find that the athletes recruited for the powerlifting group train for performance and are less concerned about their body image. By positioning their focus on objective performance (lifting as much weight as possible) this appears to have psychological benefits which helps negate negative body image as recorded in the female samples of Richardson and Chen [16] and Vargas and Winter [14]. Future research should focus on qualitative interviews with male powerlifters and additional sports to understanding the relationships between their body image and their sport.

APPLICATIONS IN SPORT The majority of previous research concerning male body image is associated with negative behaviour outcomes such as aggression, violence and or the use of PEDs [70]. This study has taken a different approach to show strength training for males has a positive outlook on their body image helping to create healthy and stable relationships with their mental health using an objective measurement. In this instance, it is the sport of powerlifting that focuses the athletes on the performance to lift as much weight as possible across three events.

Competing in a weight class sport does not necessarily produce extreme group scores and or undesirable behaviours concerning their bodyweight or body image. This implies that strength training methods such as powerlifting for males (and females as shown in Richardson and Chen [16] when seeking to improve their health and fitness are beneficial. The focus on objective strength gains via tracking their lifting through increments using progressive overload allows positive body appreciation. As a positive by-product, they will also develop improved physique through increased levels of physical activity and adherence to a training program. Furthermore, by seeing continued progressions through improving their technical proficiency doing the movements and increased muscle hypertrophy will lead to a better outlook on their mental health and body image. As they are viewing their body for its function not as an object they place less emphasis on subjective body image changes but rather on performance. In populations that include body image disorders and eating disorders, using this form of training will help support clinicians in helping return their patients to exercise routines to support a holistic recovery pathway [71].

Author roles Dr. Mark Chen: Conceptualization, Methodology, Formal analysis, Writing – original draft, Writing – review & editing, Supervision, Project administration.

Dr. Andrew Richardson: Conceptualization, Methodology, Formal analysis, Data curation, Writing – review & editing, Project administration.

Conflict of Interest Statement: The authors declare that have no conflict of interest when writing and or submitting this manuscript for peer review publication to The Sport Journal.

Funding No funding was sought or requested for the formation of this manuscript

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Decision-making on injury prevention and rehabilitation in professional football – A coach, medical staff, and player perspective

George Minoso 2024-04-08T09:47:11-05:00 April 8th, 2024 | General , Research , Sports Management |

Authors: Mads Røgen Noesgaard 1 & Stig Arve Sæther 2

1 Department of Sociology and Political Science, Norwegian University of Science and Technology, Trondheim, Norway 2 Department of Sociology and Political Science, Norwegian University of Science and Technology, Trondheim, Norway

Stig Arve Sæther Department of Sociology and Political Science Norwegian University of Science and Technology, NTNU, Dragvoll, 7491 Trondheim, Norway E-mail: [email protected], https://orcid.org/0000-0002-1429-4746

Mads Røgen Noesgaard is educated as a physiotherapist and holds a master’s degree in sport science from the Norwegian University of Science and Technology. He has an extent experience as a physiotherapist from professional sports especially related to football and handball.

Stig Arve Sæther is an associate professor in sport science at the Norwegian University of Science and Technology, with an extensive research portfolio in talent development within sports and especially football. Sæther is head of the sport science staff, head of education at the department of Sociology and Political science and head of the research group Skill and Performance Development in Sports and School (SPDSS).

Purpose The aim of this study is to research how the decision-making on RTP from the medical staff impact on the perceived short- and long-term performance of the player and the team, from a coach, medical staff, and player perspective. Methods: Two professional football players, one physical coach, one physiotherapist and one assistant coach were interviewed in-depth and recruited because of their insight, experience, and expertise from one Norwegian premiere league club. Results: The decision-making process on RTP in the club were partly based on the hierarchy in the club, where the coach was on the top among these actors. Despite that the actor´s describes the process as a natural dynamic, and felt a shared responsibility in the process, their different roles impact on the decisions. The RTP decision was affected by aspects such as the period in the season, earlier injury experience of the player and the medical staff and coach collaboration. Conclusions: Even though the medical staff and the injury prevention could mean that the player could have a longer career, the choices made in the process of RTP is often based on short term player and team performance. Applications in sport: Professional football players have competition as a living and are expected to enjoy and embrace competing against both other teams related to winning trophies and teammates related to a place on the team in matches. This degree of competition was also seen as a part of the RTP process since the competition with teammates gave the players motivation to overcome their injury situation and get back to compete for their “spot” on the team. Even though this study only includes experiences from one professional football club, it gives insight into how the RTP process is done in a professional football context. Future studies should consider recruiting representatives from the club management, which also could give insight on how the macro aspects of a club impact on the RTP decisions in the coaching team of a professional football club.

Keywords : return-to-play, professional sports, communication

The development of professional football player is complex and consist of a myriad of factors, including injury prevention and rehabilitation through the return to play (RTP) (38). Even though the development of injuries in European professional football has decreased over the last two decades (10), the impact of injuries still plays a major role in both team and individual player development and success (7). Time loss in on field training and matches may have a negative impact on the players development, which makes it vital to minimize the duration of rehabilitation and RTP process. The responsibility of injury prevention, treatment and following RTP has in the literature been described as the responsibility of the medical staff, even though a strong coach and player involvement has been recommended (10). Even so, lack of needed authority in this process, have been highlighted as a challenge since both the coaching team and especially the head coach, and the players are expected to be a part of the decision process, hereby creating a dilemma (26). The need for a high performing medical team is thereby indicated crucial for the present success, but also future accomplishments (7).

Knowing that the major predictor in future injury being previous injury (13, 27-28, 35, 45), it has become standard procedure in European professional football clubs to screen and evaluate both in-squad players and potential investments even though research points to a lack of predictive capabilities (29, 46). Hereby the screening process is arguably/potentially increasing the consequences of previous injuries and treatment of such and the importance of injury preventive measures. In the pursuit of securing the best possible squad at all times injury preventive programmes such as FIFA11+, seems common but often adjusted based on either screening results or coaches’ preferences and hereby losing its evidence-based merits (29-30, 34, 46). Another promising preventive strategy is tracking and managing of load and restitution of the individual player and indicated to both increase the “here and now” short-term performance and the long-term performance. The main aim is to reduce the risk of injuries and illness (19, 24, 36), but it also presents a risk of withdrawing players from training and matches unnecessary.

The rehabilitation process of a player must address and manage the psychological and sociological health of the player (12). Though the general plan and goals of the rehabilitation is clear there is a lack of gold-standard and consensus for RTP which complicates the last steps before returning to training and competition (22). The literature advocates a shared-decision-making process to optimize this process. Coaches, medical staff, physical coaches, and the individual player all possess insight about the state of the player seen in a bio-psycho-social framework (5-6, 8, 47). A process as such is nonetheless challenged by the different profession’s confidence in their own decision, but also potentially with a lack of trust in others, hereby creating a dilemma where authority and power becomes more important than teamwork (9-10, 20). To increase the overall medical effort, the literature advocates an SDM-approach to minimize injuries and rehabilitation periods and improve RTP (1). Still, Paul et al. newly published editorial are highlighting that there has been identified concerns surrounding the social complexities of elite sports and the difficulties of truly applying this concept in practice (37).

Most of the research on this subject and in professional football have used a quantitative approach (7) and there seems to be a need of qualitative insight on how this process unfolds in practice, and how and by whom the decisions are made. An exception is Law and Bloyce (25) who interviewed professional football managers behavior towards injured players. The results indicated that managers at the lower levels felt more constrained to take certain risks related to injured players. The aim of this study is to research how the decision-making on RTP from the medical staff impact on the perceived short- and long-term performance of the player and the team, from a coach, medical staff, and player perspective.

Participants 

Two professional football players, one physical coach, one physiotherapist and one assistant coach were interviewed in-depth and chosen based on strategic selection because of their insight, experience, and expertise in the field and their long-term involvement within one Norwegian premiere league club. The two players have in total more than 15 years in the club, while the physiotherapist and the physical coach has been in the club’s medical team for more than five years and altogether more than 20 years of experience in the field. The assistant coach has more than seven years of coaching experience. The participants are described in table 1.

All interviews were conducted in person and the location chosen by the interviewee. The length of each interview varied from 50 to 90 minutes with a mean at 70 minutes. Each interview was initiated with general questions to start the conversation and to get more background information on each participant. Prior to the interviews the questions were largely prepared to facilitate the conversation into different themes and topics of interest, with prepared follow up questions when depth and more context was needed. The questions varied specificity from general questions about the interviewee’s thoughts on the injury-period (e.g. “How do you think a player can develop while injured”) to more defined questions about the different actors’ actual role in the decision-making process about RTP (e.g. What role does the player has in the RTP-decisions). With these types of specific questions, the former mentioned extensive experience and expertise in the field was highly prioritized in the selection of participants. This made the insight in the specific club more extensive and gave the answers more depth. In addition, all participants were giving the opportunity to read through the transcript and afterwards able to withdraw parts or the interview in full, which none of the participants did. None of the participants neither wanted to alter the transcription. All interviews were audio-recorded and transcribed verbatim. By using pseudonyms for each participant, the transcriptions ensured the interviewee’ confidentiality and furthermore, ethical approval was in accordance with and approved by the Norwegian Social Sciences Data Services (number: 678375).          

Analysis The analysis of data was done with the six steps of theme-centred approach as described by Braun and Clarke (2-3). The process was initiated by the transcription by the first author who afterwards read and reread the data twice. This was followed by initial coding, phase two of the chosen method. In this process the transcription was revisited multiple times until the final codes were discovered and presented to the second writer for discussion. The total of 47 codes were structed using a mind-map, which visualised the third phase of the process and used to structure the data into nine higher-order themes. Phase four was a back-and-forth process rereading the transcript, revising the raw material for clarifying questions, reviewing the codes all in all to elaborate the emerged themes. Through dialog and discussion within the research group the final three/four themes were identified, and subgroups reviewed and hereby phase five concluded. Finally, phase 6 was a detailed process and highly interwoven with the analysis of data. To present the findings in an argumentation related to research question and to illustrate the story of the data it was important to revise the extracts and go back to the both the higher order themes and the final themes in the writing of the report to ensure that the essence of the data was captured and presented. The final report presents the experienced everyday life of the participants in this specific Norwegian Premier League football club, how they perceive the decision-making process in the context of both development and performance and how the structure and reality of modern football plays and important role in both injury prevention and RTP after injury.

The actors in the RTP process – the club hierarchy According to the actors (medical staff, coach and players), the prevention of injury and RTP practice has changed throughout the last decades, from a collective focus to a more specific and individual practice, described as a positive change by all the actors. RTP was described as a process, with benchmarks which was considered a motivational factor in the overall rehabilitation process. The decision-making process in the professional football club related to decisions on injured players and their capacity to play were affected to some degree by a hierarchy in the club. Even though the actor´s in the present study describes the process as a natural dynamic, and that they agree on their shared responsibility of the process, the different roles impact on the decisions.

Highest in the hierarchy are the coaches, and even though they highlight that the medical staff has an impact on their decision, the coaches seem to be the final decision maker in the process. This is indicated as a natural order because the coach is the one to take the ”fall” when the decisions shows to be wrong or more precisely have a negative output and also the final responsibility for the team performance. The coach described therefor a need to keep the medical staff on their toes, which the medical staff described as a challenge of their decisions, often based on what they considered external pressure on performance and results. This again meant that the medical staff had to make the “right” decision to keep their authority in the collaboration with the coaches.

The players felt in this regard that the medical staff had a two-sided role or responsibility both towards the coaches and the players, but that they still according to the players weigh the perspective of the player the heaviest. This double role was considered challenging and could mean lack of support in cases of doubt, while the medical staff considered that the final decision was taken by the coaches and the player. From the player perspective the trust was described as essential in this process. So even though trust, communication and collaboration are fundamental elements to keep a squad of players performing, there is also a need for a trust in the actors’ competencies and loyalty, both highlighted by the coach Lars: “Despite thinking about the result, first and foremost, we of course think: “The best for the player”. Because the player performs best when he is 100% healthy, both physically and mentally.” The physical coach Thomas stated this on the matter:

Thomas: “Because the vast majority of players understand deep down what the point is. They know when they shouldn’t go out there. They want to have hope, that: “yeah, it’s allright” and so sometimes our job is actually just to say: “Yes, it’s actually allright”, even if it’s 50/50, if it’s the last match on the season and they wanna take the chance anyways. Okay, then we have to see that and then just say: “This is allright”.

Thomas argued that their role in the process was to inform the coaches and even though the decision was not always in line with their suggestions, they felt that their opinions was considered vital for the final decision-making.

The factors that impact the decision process

Because of the complexity and uncertainty of who decides which players could play, the medical staff experience situations where at times they felt pressured to clear a player for playing, which in their experience often leads to a longer injury period. And despite the open communication, the pressure got more intense especially before important matches and at the end of the season, as this conversation and the following quotes indicates: Physiotherapist Hans: “You get a player who runs at 60%?”, Coach Lars: “Yes, but he is so important for us in set-pieces, so we have to have him”. This becomes even more prominent at the end of the season as physical coach Thomas highlights: “The fewer matches left, the greater chances you are willing to take with the athlete’s health”.

The decision to deny a player to train or play a match based on the risk of injury, was considered difficult for the medical staff because of uncertainty of the outcome. The coach describes how they in some cases start the player and see how it goes. Even though this was described as happening seldom and especially since this could be considered treating the players differently, which potentially could impact the team dynamic:

Lars: “If you and I play in the same position, and you train 3 times a week but you are a little better than me. I’m training every single day, and then you get to play matches. I train more than you, twice a week, and then arrangements will be made for you to play. That could become a conflict.”

The medical staff points out how this load-management strategy is potentially positive for RTP, the coach argument furthermore how this might add pressure for the next matches both for the player and the medical staff. If the team loses, one could consider that being in minus and that means that the next match must be won. This adds on to the earlier statement that an injury might be a heavy process for a player:

David: “From the moment you feel that you are a part of something, then you will show up the day after you have been injured, then you show up for work. You eat breakfast, you go to the locker room and then the rest of the team go out on field and do what you love the most, they play football. But you wander into a dark gym alone and do what all footballers think is the most boring job, cycling and doing rehab training. As boring as it gets. But you have to do it. You go into such a lonely and confined, empty mental phase, it’s really hard.”

What was considered the “right” decision depended on the perspective, even though obviously the most impacted part is the player:

Niels: “Perhaps I have been lucky in that I have not had so many major injuries, but at the same time the one injury I have had, where it was done the way it was done, that was enough for me to think: “yes, I lost some good matches that year”, then you can think of those who have been injured longer and have had more injuries, how much it has affected them.”

Injuries are however also described by all actors as a natural part of professional football, and that this often means taking risks to be able to perform on the highest level. One of the players, David, describes it as following:

David: “At the top level, you are balancing on a knife’s edge much more often, because you are pushing boundaries all the time and then the need for medical help is all the greater than when you operate at a not so fully professional level.”

It could seem from a professional players perspective that the players consider their everyday life as a footballer as finding the optimal balance to be able to stay fit and avoid injuries, and that this situation is difficult and that they need help from the medical staff to be able to keep staying “in the game”. Even so, the physical coach Lars highlights the difference between pain and injury:

Lars: “I think when you play football and it’s one-on-one, it’s dueling, you can get a knee in the side, you can get hit by an elbow, so after a football match, you might have a bruise here and a little bit of swelling there and you can have, stiffness in generel. That doesn’t mean you need 2-3 days to recover because that pain you feel”.

Protecting the players

The coach stated that it was important to protect the players and not introduce them for unnecessary risk, even though he pointed out that there is a limit in terms of how much consideration one could do for each player. In this regard did the physical coach acknowledges that there had not been a reduction in the number of injuries despite the heavy number of added resources to prevent them. The injuries have changed but one has not been able to eliminate the incident rate:

Thomas: “There is much less ankel rolls, but there are more hamstring injuries and groin injuries because there is more sprinting in the matches and the matches are closer schedueled. And you can’t quite solve that. Even with sufficient sleep, enough nutrition, tablets in the fusion of plasma, i.e. “you name it”, game ready – the player still breaks down and then you see that if you train very well, then maybe you will go through the season with very little damage.”

This was also something the players describes as problematic in certain situations, as stated by Niels: “Coach, physio and they, they really push you back in and then it’s difficult as a player to sit there and say: “I’m not healthy”, it’s difficult!”

The physical coach recons it is all about the time spent on the pitch to improve RTP and the high amount of matches impact on the possibilities for the medical staff to schedule and complete the injury preventions and rehabilitation. One example mentioned are an away match where the travel time is the reason for the player not attending enough training sessions, even though he is ready to train.  Furthermore, the game importance is an important factor because of the impact on the results sportingly and economically and has been found to be the reason as to why players play partly injured, or at least adding on to the pressure on the medical staff and their decision on every player potentially injured.

          Also, one of the players described how he perceived that the players are at their best when the get to train and play matches as much as possible:

David: “All footballers perform at their best when they get the opportunity to play football every day. Play every match. That’s when you get into a rhythm, where you act on intuition in battle and in that moment. In order to do that, you have to have continuity in your training and to have that, you have to be good at taking care of your body, to manage and last through a tough week of training, to perform in every match. So it’s definitely important. You profit from doing a good job (ed. injury prevention) in order to be able to perform in the best possible way. It is absolutely indisputable.”

Both the players and the medical staff highlights that the injury prevention is important for the players to be able to train more.  The physical coach highlights that this injury prevention training has a direct impact on the player opportunity to run faster and develop more power.

One of the players mentions how each club and their culture try to maximise the development and that the club culture is impacting the performance. This was also mentioned by the coach who stated that building the club is one of the most important tasks for the club, which is considered difficult since both players and coaches comes and goes. Another challenge is the impact the head coaches have on how the club perceive injury and development. The physiotherapist describes how the many changes also impact on the medical staff and their way of working:

Hans: “I think that, the biggest challenge in all of this is the constant change in player material, the constant change, at least as it has been in X, that coaches change, and therefore you constantly have different routines. It is natural that a coach who comes in and is boss wants to have it his way, and then a new coach comes in who wants it his way. Then there will always be changes and that means that what you tested on last year will be tested in a different way this year.”

Both players and the physical coach add on to this position, even though they also see positive outputs when new people are trying to collaborate:

Thomas: “Things that work well can also be diluted by poor execution. I think we make it work. I think so. that’s how it is when you bring new things to the table. Basically, it should be a good thing and if you manage to get best out of it, then it will be beneficial.”

The injury situation as an opportunity for development

All the actors thought of the injury period as a period for potential development of performance level of the player. So even though the players considered it as a tough and challenging period, it also contains opportunities. The coach highlighted that this motivation and opportunity had to come from within, and that he medical staff and the coach’s role was to facilitate and further motivate. In that way the injury period can be effective and also an opportunity, which could be considered a win-win situation both for the player and the team. 

Still, at times the players felt pressured to play, and sometimes felt alone and “naked” in the discussion between them, the medical staff and the coaches. This was partly confirmed by the physiotherapist, who described football as being black or white at times, and that he felt the need to protect the player:

Hans: “A player who is out several times and often… It can very quickly become black and white in a football club, “This player is always injured. No, we’ll give up on him a little”, and then it’s challenging to say: “You mustn’t give up on him, even if he’s a bit injured now. There are several factors that cause him to be injured and we have to look at ourselves as well, all of us.” What we have often done is to look at the coach and say: “If we are going to get him out of this, we’ll have to make a change. What we are doing now is not good enough. So we have to take him out of training and have to do this instead of that. He can’t play every game and at the moment”.

However, at other times the medical staff also feel the need to push the players to return to ordinary training or playing matches. They feel the need to be careful since they might misstep. Some players might get pushed back to soon, while others need a push.

Lars: “Sometimes where you have to push a little, and we really do that for the sake of the player, not because we absolutely have to. We don’t take any chances with players, that is. But if we see that he has done what he is supposed to and at the same time it is a player who is a bit more careful with himself. Because that too, you have to know the group, you have to know the player, because there are some who can be too tough too early, and then there are some who are actually ready, but holding back. So you can say that sometimes we have to try and push them in a positive way too, I think. Without us doing anything wrong.”

One of the players Niels stated that for some of the players, they need to be more included in the decision-making-process. One example mentioned by one of the players was the importance to get into the pre-season together with the squad, to be able to compete about his playing position.

The medical staff clearly stated that they did not consider themselves having the definitive solution in every case. They also mentioned the fact that holding a player back from a match based on the fear of being injured might deprive the player from development and potentially economic gain (e.g. club transfer, bonuses etc.) or the team’s performance or the club’s economic gains. Many of the actors highlighted that if the player felt ready to play, and the coaches meant that he would have an impact on the game, the medical staff would take that into consideration. This position of taking a decision which is good for all the actors both in a short-term and long-term perspective was considered a difficult dilemma for the medical staff, since they feel an extra responsibility related to the players health.

Keeping the players on their toes but still together

The coach also highlighted that the competition between players could challenge the individuals in the club. Internal competition is essential and when a player is injured, that could create an opportunity for other players. This competition was also highlighted by the two players, however as a stressor for the injured player. The coach however stated that this type of competition must be present and that it makes the players push each other, and fight for a place on the team. This type of pressure, trying to withhold your place on the team, having the right attitudes, frequent changes in the coaching staff, and short-term results, was describes from all the actors as impacting the medical staff’s opportunity to impact the decision for players to play matches and their development. Both the coach and the medical staff highlighted that this might impact the decision, but never determined the RTP, while the players could consider this as a weighty stressor

The players point out a potential isolation of the injured players by dividing the players into two groups: those who are injured and those who are not, but this division is described differently based on the perspective. They also describe the rehabilitation as lonely, heavy, and boring, especially the acute phase, and experience that the injured players not to be a part of the community in the club, which the player Niels described in the following: “But I want to put it this way, you are down in hell and then you start the ascent from there, and then it becomes a bit like tunnel vision. You don’t see the light at the start, but you see it eventually”. The coach, however, does not describe this as an isolation or division of the team, but rather a natural part of the everyday life in a club, but highlight the importance of joint meals and meeting schedules. The medical staff have another nuance of this division, since an injury might be challenging and create a sense of exclusion, while this could also be good for the team, since the negativity which often comes with an injury does not get spread among the other team members. The physical coach highlights the same and furthermore that it should be attractive not to be injured.

All the actors describe the deprivation from matches in times of doubt about a player’s availability have both sportingly and economic negative impact on the player’s career:

David: “Football can be so simpel that if you, how should I put it , score a hat-trick in the right match against the right team, you can be like… And the salaries are so high, so if you end up in the right place then you, then you can in a way support the whole family for the rest of your life. So it’s quite clear that injuries affect the course of a career.”

Injuries means less time to train, and the actors agree that the time for the specific football training and matches are essential for a player’s individual development. Both the coach and the physiotherapist highlighted however the importance of making the most of the injury period, which could be considered as a window of opportunity to focus on individual skill development, which normally one does not have time for. The physical coach stated however that it might be difficult for a player to develop largely during the rehabilitation process. And this could be related to the somewhat black-white perspective the medical staff and the coach has on injuries. The physiotherapist meant that this approach might have a positive consequence for a player who have experienced an injury. They often work harder than before to be able to get back to football. At the same time Hans also pointed to the fact that the players could be “forgotten” by the coaches if they achieve a “bad” reputation: “But if you first get a reputation of being.. that the coach gets the feeling that he is not available, then it can often be difficult. A fight really. That is my experience”. The coach Lars partly confirmed this by stating that the coaches are aware of players who have a history of injuries, which often mean that they cannot play all matches during a season:

Lars: “In other words, injury follows injury. It’s a bit like that. So there are certain players that you know more or less that he is not going to play 100% of the games. Let’s say there is an exclusive player who often gets hamstring issues, then you know that during the season he will play 70% of the games. It may happen that we have players, who we know are like that.”

In a long-term perspective and focusing on the players career, the coach also highlighted that the players are screened and assessed by clubs if a club transfer is in motion, that a player with a large injury history would be considered as less interesting to recruit:

Lars: “[…] But the more players who don’t have an injury history.. So if you’re going to build a team then you have to get as few players as possible with an injury history, because often you see that those type of issues, especially if it’s the groin or hamstring or those types of injuries, they often come back.”

The coach described players’ injury history as essential when clubs assess which player they could recruit, and that injured players must convince the coaches to become relevant for a club transfer. These types of assessment are important for coaches in their process of building a squad both in a short-term and long-term perspective.

The aim of this study is to research how the decision-making on RTP from the medical staff impact on the perceived short- and long-term performance of the player and the team, from a coach, medical staff, and player perspective. The decision-making process on RTP in this professional football club were partly based on the hierarchy in the club (40). So, despite that the actor´s in the present study describes the process as a natural dynamic, and felt a shared responsibility in the process, their different roles impact on the decisions. The coaches were described highest in the hierarchy and related to them being responsible for the sportingly results and the performance of the team. The players were described as having a say in the decision of his availability, even though they often highlighted an experience of being pressured to play in certain situations (9). The medical staff was considered to have a two-sided role, since they were employed and a part of the coaching team and naturally felt a responsibility on behalf of the coaches and the club, they also felt the need to protect the players and their health as professional health workers (20). Their decisions would often mean that they had to “disappoint” the coaches or the player, by denying the player to play or the availability of a player in a match.

Responsibility was a term especially the medical staff used to describe how they felt about their role, but also when taking part in the final decision in the RTP process. This responsibility became important in the process of making “the right” call based on the information available while trying to account for the interests of all the actors. This might mean that they let a player play, with a “let´s see how it goes” approach, and that the outcome of the decision was described as “right” if the player played the whole game. A dilemma in the process was also related to the natural part of pain and injury as part of professional football described by all the actors in the process (31). So even if protecting the players was important, time spent on the pitch is the main goal for both the individual players and the team’s development and performance. Even so, earlier research (41) has indicated that elite sports have a pain culture where pain is a natural and expected part of elite sports, which could have a negative impact on the players development, if this means that the players do not communicate when feeling injured or unavailable for training and matches.

Professional football is all about results and performance (32). So, a characteristic off successful environments is their constant search of areas to develop further (14). This seemed to be the case in this club as well since a period of injury was considered an opportunity for the player to develop. The players are competing about a place in the starting line-up and need to pick up the glow to get back into the team. Still, there was also a mutual understanding that each RTP case might be different and had to be considered individually. So, in some cases both the medical staff and the coaches felt that some players needed a push to get back. This may in many cases also be in the best interest of the player since it could mean that they in example get identified by scouts, impacting their career by a club transfer. Furthermore, this pressure could mean that the players are willing to take a higher risk by playing while injured. The players in this study described being injured as lonely and feeling isolated from the team, as found in earlier studies (32), which could be perceived as an increased motivation to RTP potentially even before the mind or body are ready.

In accordance with the focus on results and performance in professional football are also the high degree of uncertainty in this professional context (15). This could be related to the small margins between success and failure. This is also related to the RTP process, since all actors in the process of RTP must make the best decision for both the individual and teams’ performance. Still, there is a lack of knowledge related to the potential outcome of the decision. This means that the actors must “take risks” to be able to maximize the opportunity to succeed. While it was not a part of the study, the obvious economically benefits of decreasing time loss in training and competition on both an individually (players, medical team, and coaching team) and club level (potential sale of players), also makes both the rehabilitation and preventive strategies important. The club perspective might conflict with the individual actors in the RTP process, with the example of the club winning the league, while a player got injured because of the overload and hereby potentially ending his career.

All the actors in this study highlight that football is a sport where you must expect to feel pain regularly and that injury is a part of being a professional football player. So even though the medical staff and the injury prevention could mean that the player could have a longer career, the choices made in the process of RTP is often based on short term player and team performance. Professional football players have competition as a living and are expected to enjoy and embrace competing against both other teams related to winning trophies and teammates related to a place on the team in matches. This degree of competition was also seen as a part of the RTP process since the competition with teammates gave the players motivation to overcome their injury situation and get back to compete for their “spot” on the team. Even though this study only includes experiences from one professional football club, it gives insight into howe the RTP process is done in a professional football context. Future studies should consider recruiting representatives from the club management, which also could give insight on how the macro aspects of a club impact on the RTP decisions in the coaching team of a professional football club.

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Comparing Public vs. Private High School Sports-Related Concussions from a Countywide Concussion Injury Surveillance System

George Minoso 2024-03-18T11:04:24-05:00 April 5th, 2024 | General , Research , Sport Training |

Authors: Gillian Hotz 1 , Jacob R. Griffin 2 , Hengyi Ke 3 , Raymond Crittenden IV 4 , Abraham Chileuitt 5

1 Department of Neurosurgery, University of Miami Miller School of Medicine, Miami, FL, USA 2 KiDZ Neuroscience Center, The Miami Project to Cure Paralysis, University of Miami Miller School of Medicine, Miami, FL, USA 3 Department of Public Health, Division of Biostatistics, University of Miami Miller School of Medicine, Miami, FL, USA 4 Department of Neurology, University of Miami Miller School of Medicine, Miami, FL

Gillian Hotz, Ph.D. 1095 NW 14th Ter Miami, FL 33136 [email protected] 305-243-2074

Gillian A. Hotz, PhD is a research professor at the University of Miami Miller School of Medicine and a nationally recognized behavioral neuroscientist and expert in pediatric and adult neurotrauma, concussion management, and neurorehabilitation. Dr. Hotz is the director of the KiDZ Neuroscience Center, WalkSafe, and BikeSafe programs.

Purpose Largely, research on adolescent sports-related concussion (SRC) has focused on public school athletes. SRCs of private school athletes have been studied less and may differ due to differences between school types.

Methods SRCs between Miami-Dade County high school athletes at trained public (n = 1088), trained private (n = 272), and untrained private (n = 79) were compared. Outcomes included days between date of injury (DOI) and clinic date, days between DOI and post-injury ImPACT retest, days withheld, return to play (RTP), ImPACT baseline and post-injury retest completion, and academic accommodation status.

Results Trained public and trained private groups had similar days between DOI and clinic date, days withheld, and percentage who RTP. Differences between the trained public and untrained private groups existed for RTP but not for days between DOI and clinic date or days withheld. Private group athletes were more likely to receive academic accommodations.

Conclusions Public and private high schools trained on the same SRC protocol did not have significantly different outcomes. The untrained private schools, however, had worse outcomes compared to the public group.

Application In Sports SRC outcomes in both public and private high schools may benefit from SRC education, training, an established protocol, and use of a management system.

Keywords : youth athletes, concussion recognition, concussion management, private schools, sports

Each year, an estimated 1.6 to 3.8 million sports-related concussions (SRCs) occur in the United States (1). While the nearly 8 million high school athletes participating in sports annually benefit from the improved social, psychological, and physical health gained from playing sports (2, 3), there is also an ongoing risk of injury due to consistent athlete-exposure (4). SRCs are understandably a concern for high school aged athletes due to the short-term and potentially lifelong behavioral, cognitive, emotional, physical, and psychological effects they can produce (1, 5). These consequences can be particularly worrisome as this population is already experiencing their own ongoing physical and cognitive development changes that can negatively be affected by an SRC (6). Understanding risk factors contributing to adolescent SRCs and what may lead to differences in outcomes is therefore imperative for identifying those most at risk and ensuring the proper management and treatment resources are in place.

Thus far, an overwhelming majority of research on SRCs has focused on or included samples of public high school athletes as opposed to private high school athletes. One example is the High School Sports-Related Injury Surveillance Study, Reporting Information Online (RIO) (7). The High School RIO is an internet-based data collection tool that captures athletic exposures and injury events through athletic trainers (ATs) that report data. It is often used as a source of SRC data for research (4). In the most recent report, nearly 80% of the participating high schools were public with the rest being private (7). Additionally, other studies on SRC incidence and trends have included only athletes from public high schools (8.) The lack of private high school inclusion in adolescent SRC research is an important consideration because known distinctions between public and private high schools possibly lead to differences in SRC incidence and outcomes (4). These include differences in school size, support services and resources, student racial/ethnic backgrounds, rigorous academic programs, and socioeconomics (9).

While there has been recent research that details private high school athlete SRC experiences and reporting behavior (4, 10), there is still a need for continued research into private high school SRC outcomes. Specifically, it would be important to examine how SRC outcomes differ between public and private high schools. Therefore, the purpose of this study was to compare SRC outcomes between public high schools who received specific concussion training and education to private high schools who received the same training and private high schools who did not receive training on the same SRC protocol. The goal of using these three distinct groups was to examine whether differences in SRC outcomes would be a result of differences in SRC education, training, and protocol.

Participants and Procedures

This study included Miami-Dade County (MDC) public and private high school athletes with an SRC that occurred in a practice or game between August 1 st , 2012, and July 31 st , 2022. All athletes were treated at the University of Miami Miller School of Medicine’s Concussion Clinic, UConcussion (UCC). Athletes that sustained an SRC outside of the study period were excluded as well as those with an SRC that did not occur during an MDC public or private high school practice or competition. If an athlete was treated at a provider other than the UCC, they were also excluded. 

 The UCC clinical team hosts an annual SRC training and educational workshop for MDC public high school ATs and athletic directors (ADs). In these workshops, ATs and ADs are trained on how to use the Six Steps to Play Safe protocol (11) and how to administer ImPACT (12) concussion tests. The UCC also makes available specialty concussion clinics where athletes with a suspected SRC can be referred to for management and treatment. The UCC similarly partners with and provides training and education to 8 private high schools within MDC. While athletes at other private high schools within MDC can still be referred to and receive treatment at the UCC, ATs and ADs at these high schools are not provided with the same educational workshops and training on the Six Steps to Play Safe protocol (11). In this study, there were 35 trained public, 8 trained private, and 29 untrained private high schools that were grouped as either “trained public,” “trained private,” or “untrained private,” respectively.              

The Six Steps to Play Safe (11) is a standardized protocol that can be used to manage an athlete’s SRC and safe return to play (RTP) and return to school during recovery (Figure 1). Included in this protocol are, in order, pre-season ImPACT (12) baseline testing, AT sideline testing, post-injury ImPACT testing, SRC clinic follow-up, gradual RTP and return to learn protocols, and SRC injury surveillance form completion.

Variables Reported variables were collected during UCC visits and from surveillance reporting by ATs. Athlete information in the study included demographics and the sport played when the injury occurred. SRC specific information was also reported and included date of injury (DOI), days between DOI and first clinic date, days between DOI and post-injury ImPACT retest, RTP status (yes/no), and days between DOI and RTP (days withheld). To eliminate the few extreme outliers, athletes were only included in days between DOI and first clinic date as well as days withheld mean calculations if the value for these variables was < 120 days. For similar reasons, only athletes with days between DOI and post-injury ImPACT retest < 30 days were included in the calculation. Whether an athlete received academic accommodations was included as a variable because previous research (13) suggests that private high school students experience particularly high levels of stress due to concerns about academic performance and school requests, which potentially impacts whether academic accommodations are prescribed. The percentage of athletes who experienced loss of consciousness (LOC) was also reported because LOC indicates a potentially more severe SRC and is associated with longer recovery than SRCs without LOC (14). Athlete ImPACT (12) baseline testing and post-injury data from the ImPACT test online database was included and used to determine whether athletes had completed a baseline ImPACT test and/or a post-injury ImPACT retest. ImPACT testing comparisons were only included for the trained public and trained private high schools since untrained private high schools either did not use ImPACT or did not grant the UCC access to their records.

Data Analysis Data analysis was performed using R 4.2.2. Athletes sustaining an SRC from MDC public high schools were compared with athletes from private schools between 2012-2022. The eight private schools were particularly selected because they followed a similar protocol and received the same SRC education as the public schools. The other 29 private schools did not receive the training or follow the protocol. For continuous data in the normal distribution like “Age”, mean and standard deviation were reported. For categorical data, such as “Gender”, data was presented as frequency and percentage. For those variables with important clinical significance, such as “Days withheld”, data was reported as median and interquartile range. Propensity score matching was performed to match the public schools with the eight private schools who received similar SRC training. SRC outcomes were therefore compared between trained public and trained private schools before and after matching. This was done to confirm whether one hypothesis, that public and private schools trained on the same SRC protocol would not differ in SRC outcomes, would be true when baseline covariates were and were not controlled for between the groups. Sample T-test was used to detect the significant difference for quantitative data in the normal distribution. The Wilcoxon test was used for quantitative data in non-normal distribution. The Chi-Square test was used to detect significant differences in categorical data. Statistical significance was set at < 0.05.

Participant Demographics A total of 1,088 public, 272 trained private, and 79 untrained private athletes were treated at the UCC during the study period and are included in this study. The average age was similar for each group (16.5 and 16.2). While there were more male than female athletes in all three groups, the percentage of athletes that were female was greater in the trained (38.6%) and untrained (38.0%) private groups than the public group (25.9%). In both the trained and untrained private groups, a greater percentage of athletes were White (28.5% and 25.3%) or Hispanic (62.6% and 68.0%) compared to public athletes (8.0% White, 56.4% Hispanic). The public group instead had a greater percentage of Black athletes (30.9%) than the trained (24.7%) and untrained (6.7%) private groups. Across all three groups, football accounted for the greatest percentage of SRCs but was more prevalent in the public (58.3%) than both private groups (36.4% and 39.2%) (Table 1).

Comparing Trained Public and Trained Private High Schools SRCs Data from trained public and trained private high schools was compared to determine if there were any differences in outcomes between public and private high schools that were trained using the same protocol and program. There were no differences between the groups for days between DOI and first clinic date (P = 0.1), days withheld (P = 0.83), post-injury retest completion (P = 0.06), and RTP (P = 0.30). The average days between DOI and post-injury ImPACT retesting was smaller (P < 0.001) for the public (3 days) than trained private (6 days) group. The public group also had a greater percentage of athletes who completed ImPACT baseline testing (88.5% vs. 80.1%; P < 0.001). The trained private group had a significantly greater percentage of athletes who had academic accommodations (P < 0.001) and experienced LOC (P < 0.001) (Table 2).

After matching, groups had similar demographic characteristics for age, sex, race, grade, and sport (Table 3). Outcomes between the matched groups were also compared, and there were no differences for days between DOI and first clinic date, days withheld, percentage of athletes who completed ImPACT baseline testing and post-injury retesting, and RTP (Table 4). However, average days between DOI and post-injury ImPACT retest was smaller for the public group (4 vs. 6 days, P < 0.001). The public-school group was also more likely to have experienced LOC (P < 0.001) and not receive academic accommodations (P < 0.001).

Comparing Trained Public and Untrained Private High School SRCs Trained public and untrained private groups did not differ in average days between DOI and first clinic date (P = 0.40) or days withheld (P = 0.40). A significantly greater percentage of the public group did RTP (91.9% vs. 81.0%; P = 0.002). More of the athletes in the untrained private group received academic accommodations (P < 0.001) and experienced LOC (P < 0.001) than did the trained public group (Table 5).

Understanding risk factors, whether demographical (e.g., sex, age) or injury event-related (e.g., sport, mechanism of injury), that are associated with differences in SRC outcomes are important for ensuring that those most at risk receive proper SRC treatment and resources. One potential risk factor that was explored in this study was whether an athlete was from a public or private high school. Historically, most research on SRC risk and outcomes has been conducted using public high school athletes (4). This study provides further insight into how SRC outcomes between high school athletes differ based on the type of school attended and if a dedicated SRC protocol and education can help mitigate any differences.

While football accounted for the greatest percentage of SRCs in all three groups, its contribution was roughly 20% percent more in the public group than both private groups. Other sports, including soccer, basketball, and volleyball, were more prevalent in both private school groups. The distribution of sport played during the SRC injury event likely differed between public and private groups because private schools offer a variety of sport options, like crew and sailing, that were not available at public schools. This availability may have impacted the popularity of sports and participation numbers as private school athletes had a greater number of sports to choose from.

To our knowledge, there is only one other study (15) that directly compares SRC experiences between public and private schools. In that study, private school athletes were twice as likely to report a history of SRC compared to public school athletes, but there was no difference in RTP timelines between athletes at the different types of school (15). While the current study did not compare history of SRC between school types, analysis was performed to compare rates of RTP. There was no significant difference between the trained public and trained private school groups for RTP percentage or days withheld (Table 2), similar to the other study that concluded no difference in RTP. After matching, there was still no difference in RTP percentage or days withheld between these groups (Table 4). The untrained private group, however, had significantly less athletes RTP than the trained public group (Table 5). The UCC is a specialized concussion program that provides comprehensive SRC management and treatment, but the program also provides continuing education and a standardized protocol to the trained public and private high schools to better identify, manage, and treat athletes with an SRC (11). Athletes at these participating trained high schools potentially benefited from the coordinated and structured care they receive as a result of these trainings and partnerships, which may have led to better RTP outcomes compared to the untrained private group. These results also suggest that SRC outcomes do not necessarily depend on school type and the systematic differences between public and private schools (4, 9), but instead on AT and AD SRC education and if an SRC protocol is in place and being followed. Additionally, these results also indicate the positive effect an available and established SRC program and protocol with clinicians trained on SRC management and treatment can have on SRC outcomes. Another finding was that the trained public and untrained private groups did not differ in average days between DOI and first clinic date (Table 5). Systematic differences in socioeconomics between public and private high schools (9) may explain why the trained public group did not have significantly fewer average days between DOI and first clinic date than the untrained private group, which was the initial hypothesized result. There is well established evidence (16) that supports a relationship between socioeconomics and access to healthcare, and socioeconomic differences between school type may have led to barriers, including transportation, time, and costs, that delayed public athletes from getting into the UCC (17). Yet, there was also no difference between trained public and trained private groups for average days between DOI and first clinic date in both unmatched and matched comparisons (Tables 2 and 4), suggesting that UCC’s partnership with these schools and the flexibility it provides by offering both on-site and virtual appointments may have alleviated any potential differences. These findings also indicate that educating ATs and ADs on the risks of SRCs leads to quicker identification and subsequent appointments.

The percentage of athletes who received academic accommodations after an SRC was significantly greater for both the trained (unmatched and matched) and untrained private school groups compared to the trained public school group. During recovery from an SRC, athletes may have post SRC symptoms that can interfere with their ability to participate and function in the classroom setting (18). Consequently, return to learn protocols and academic accommodations are often provided to the athlete to help reintegrate them into classes but also prevent worsening symptoms (19, 20). Previous research (13) shows that private school students face a particularly high level of academic pressure, potentially due to more rigorous academic programs (9), which could explain why a greater percentage of private groups in this study received more academic accommodations. These additional academic accommodations may have been provided to reduce the burden private group athletes felt about their academic responsibilities or at the request of academic advisors employed at these schools. However, it is important to ensure that all athletes with a sustained SRC receive any appropriate and necessary academic accommodation, regardless of school type attended, to prevent further symptom development.

Limitations This study is not without limitations. All participants in this study were athletes that attended a public or private high school in MDC. Results may not be generalizable to other playing levels, like youth, middle schools, and college, nor to public or private high schools in other counties. Additionally, while other counties may have their own SRC surveillance system, they may not have a program, such as the UConcussion program, that provides ATs with additional SRC training and encourages timely, accurate reporting. A larger sample population in all three groups would have also been beneficial and provided more evidence on the impact of SRC education and protocol on SRC outcomes in the high school setting.

Public and private high school groups trained on the same SRC protocol did not have significantly different SRC outcomes. The untrained private high school group, however, had worse SRC outcomes compared to the public school group, suggesting that SRC outcomes in the high school setting may benefit from education, training, and an established SRC protocol and program and not on whether the school is public or private.

Applications In Sport

An inherent risk of playing sports is injuries, and SRCs are a particularly concerning injury for high school athletes, especially those playing contact sports. Ensuring those responsible for helping to manage SRCs in high schools are educated about SRCs is important, and a collaborative approach to treating and managing SRCs has been recommended (20). As suggested by this study, all high school personnel involved with athletics should be offered SRC management training and education to help improve outcomes of those that sustain an SRC. Additionally, an SRC protocol, like the Six Steps to Play Safe (11), should be established and can include:

• Pre-season baseline testing, using computer-based tests such as ImPACT (12)
• Sideline testing after a potential SRC injury (SCAT5, Balance Error Scoring System (BESS), etc.)
• Post-testing after a suspected SRC (to compare neurocognitive scores to pre-season baseline tests)
• Clinic appointments with a healthcare professional trained in SRC who can evaluate tests and make recommendations
• Gradual RTP and return to learn protocol after the athlete has been examined by a professional and is asymptomatic
• Injury surveillance system reporting by ATs

ACKNOWLEDGEMENTS The authors would like to thank: Dr. Kaplan and the UHealth Sports Medicine Clinic and Staff, the Division of Athletics and Activities for the Miami-Dade County Public Schools, all Miami-Dade County High School Certified Athletic Trainers, previous UConcussion team members, Dr Kester Nedd who served as medical director of the program from 2012 to 2019, current medical director Dr. Abraham Chileuitt, and The Miami Dolphin Foundation for supporting countywide ImPACT testing and educational workshops. We also want to thank David Goldstein and the Goldstein Family for the development of the Countywide Concussion Care Program and their initial and continued support. The project was supported by the University of Miami Clinical and Translational Science Institute.

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An analysis of the factors impacting win percentage and change in win percentage in women’s Division 1 college lacrosse

George Minoso 2024-04-01T07:01:38-05:00 March 22nd, 2024 | General , Research , Sport Training , Sports Management |

Authors: Christiana E. Hilmer 1

1 Department of Economics, San Diego State University, San Diego, CA

Christiana Hilmer, PhD 5500 Campanile Drive San Diego, CA 92182-4485 [email protected] 619-301-9388

Christiana E. Hilmer, PhD, is a Professor of Economics at San Diego State University in San Diego, CA. Her research interests include the economics of sports, applied econometrics, labor economics, and resource and environmental economics.

What factors in women’s NCAA Division 1 college lacrosse led to an increase in win percentage in a single season and a change in win percentage across two consecutive seasons? Do these factors differ between teams at the top and the bottom ends of the win distributions? Using data from the 2023 and 2022 lacrosse seasons, we find that goals, assists, unassisted goals, and participation in the NCAA Championship tournament have a positive impact on win percentage, while opponent’s goals and if the team was new in 2023 have a negative impact on win percentage. The most crucial factor that explains the change in win percentage between the 2022 and 2023 lacrosse seasons is an improvement in the change in total shots ratio, while changes in attacking efficiency and defending efficiency are also important, all together explaining 58% of the variation. Teams at the bottom of the distributions have similar characteristics for both win percentage and change in win percentage as those teams in the middle and the top of the distributions, although there are some slight differences in the magnitudes of the statistically significant variables. These results suggest that lacrosse players and coaches should focus on obtaining additional goals and assists while concurrently minimizing the opponent’s goals to increase win percentage and changes in win percentage.

Keywords : distributional impacts, quantile regression, women’s college lacrosse

Since the advent of sabermetrics pioneered by Bill James and the popularity of Lewis’s (5) Moneyball, the use of statistics to analyze sports has exploded in popularity. Reep and Benjamin (7) applied statistical analysis to team-wide factors in soccer where they investigated how the passing skill and position of a player on the field impacts goals. When analyzing a team’s performance, it is essential to determine which factors lead to a team’s success. Most research in this field has focused on professional sports. Busca et al. (1) examine eleven high-stakes international soccer tournaments to determine where a penalty kick is most likely to be struck. Pelechrinis and Winston (6) develop a framework that is comprised of publicly available data to determine the expected contribution of an individual professional soccer player to the probability of his team winning the game. Alberti et. al. (1) examine goal-scoring patterns in four different professional soccer leagues and find that the majority of goals are scored in the second half of the game with the most goals being scored in the last fifteen minutes of play. Castellano et. al. (3) analyze professional soccer match statistics to determine which factors impact winning, drawing, and losing a game and find that shots, shots on goal, and ball possession are important on the offensive end of the field, while total shots received and shots on target received are important on the defensive end of the field. A notable departure from research that focuses on professional soccer is Joslyn et al. (4), who examines the factors that improve the change in win percentage in men’s Division 1 (D1) college soccer. They find that improving shots, attacking, and defending positively impact the change in win percentage between two consecutive seasons.

This research utilizes the tools found in the team-focused literature from soccer and extends it to lacrosse. Soccer and lacrosse have many similarities, especially regarding possession, assists, goals, and defense. There are also marked differences between the two sports in addition to the obvious one: in soccer the ball is kicked while in lacrosse the ball is played with a net attached to a stick. Lacrosse is a higher-scoring game due to the presence of a 90-second shot clock and defending a women’s lacrosse player is more difficult in lacrosse than it is in soccer. One reason for this is that in lacrosse it is a foul to “move into the path of an opponent without giving the opponent a chance to stop or change direction, and causing contact” (page 51, 2022 and 2023 NCAA Women’s Lacrosse Rules Book (6)), while there is no such rule in soccer. Another reason is due to a rule in women’s lacrosse called shooting space (page 54, NCAA 2022 and 2023 Women’s Lacrosse Rules Book (6)), which states that “with any part of one’s body, guarding the goal outside or inside the goal circle so as to obstruct the free space to goal, between the ball and the goal circle, which denies the attack the opportunity to shoot safely and encourages shooting at a player” while soccer does not have a comparable rule. According to NCAA Statistics (7), the average number of goals per game scored in D1 women’s college lacrosse in 2023 was 12, while the average number of goals per game scored in D1 women’s college soccer in 2023 was 1.39. Another notable difference between lacrosse and soccer is that the offside rules are very different. The offsides rule in lacrosse states that there must be at least five defenders behind their defensive restraining line and at least four offensive players behind their offensive restraining line (page 61, NCAA 2022 and 2023 Women’s Lacrosse Rules Book (6)). The offsides rule in soccer is much less stringent and it states that when in the opponent’s half of the field “the player is not closer to the opponent’s end line than at least two opponents” (page 52, NCAA 2022 and 2023 Soccer Rules Book (7)). These disparities between lacrosse and soccer may result in differences in which factors impact win percentages and changes in win percentages.

This research examines which factors lead to an increase in win percentage and change in win percentage for women’s Division 1 college lacrosse teams. We also seek to determine if these factors differ among teams in the 25th, 50th, and 75th percentiles for win percentage and the change in win percentage. Using data from the 2023 women’s D1 college lacrosse season, we explain 86% of the variation in win percentage. Goals, unassisted goals, and participation in the NCAA Championship tournament have a statistically significant positive impact on win percentage, while opponent’s goals and if the team was new in 2023 have a statistically significant negative impact on win percentage. The most crucial factor explaining the change in win percentage between the 2022 and 2023 lacrosse seasons is an improvement in the change in total shots ratio, while changes in attacking efficiency and defending efficiency are also statistically significant, all together explaining 58% of the variation. The variables that explain both win percentage in a single season and the change in win percentage between seasons are similar between the 25th, 50th, and 75th percentiles. This suggests that teams at the bottom of the distributions should focus on the same factors as those at the top when they seek to improve during a season and between seasons.

Data Source Win percentage was collected from the National Collegiate Athletic Association (NCAA) archives for the 2023 and 2022 seasons. A win was awarded one point while a loss was awarded zero points. Offensive and defensive statistics for the 2023 and 2022 seasons were collected from each University’s women’s lacrosse website housed in the season’s cumulative statistics. It is important to note that these data are provided by individual institutions and therefore the statistical findings of this research is dependent on the accuracy of the information provided by each school. In addition to winning percentage, data was collected on goals, assists, shots, opponent’s goals, opponent’s shots, unassisted goals, ground balls, turnovers, caused turnovers, draw controls, whether the team was new to NCAA D1 lacrosse in the 2023 season, and if the team made the NCAA Championship tournament in 2023. Of the 126 D1 women’s lacrosse teams, 123 had information on every variable listed above.

Variables and Distributions

This analysis aims to determine what factors impact a single season winning percentage and which factors impact the change in win percentage across two consecutive seasons. Figure 1 is a histogram of win percentage for the 2023 women’s lacrosse season. The average win percentage was close to 50% at 48.27%; the minimum win percentage was 0 for the two teams that lost every game during the season, while the maximum win percentage was from a team that won 95.65% of their games. The team with the second-highest win percentage won the 2023 NCAA National Championship tournament.

Summary statistics for the 2023 D1 women’s lacrosse 2023 season are found in table 1. The average number of goals and opponent’s goals nearly offset each other at 211 and 210, respectively. There was an average of 495 shots with a large standard deviation of 105. Below half the goals were aided by an average of 92 assists, while over half of the goals resulted from an average of 119 unassisted goals. There were nearly twice as many turnovers as there were caused turnovers, 7% or a total of 8 teams were new D1 lacrosse teams in 2023, and 24% of the D1 lacrosse teams made the NCAA end-of-season tournament.

Figure 2 contains a histogram of win percentage change, which is constructed by taking the win percentage in the 2023 lacrosse season and subtracting the win percentage in the 2022 lacrosse season. There are fewer observations in the change in win percentage because the seven teams who were new in the 2023 season did not have any statistics for the 2022 season. On average, most teams had a similar win percentage in 2023 as they did in 2022, with an average change in the win percentage of .16. The team with the lowest change in win percentage between the two seasons of -51.47 had a win percentage of 75% in 2022, dropping to 24% in 2023. At the other end of the spectrum, the team with the highest change in win percentage won 12% of their games in 2022 and improved to winning 50% of their games in 2023.

Following Joyce et al. (4), we construct three measures of team success to explain the change in winning percentage: total shots ratio, attaching scoring efficiency, and defending scoring efficiency. The first measure, total shots ratio, is constructed as

The total shots ratio in both 2022 and 2023 is .5, which means, on average, teams are matching their opponent’s shots with their own shots with a range in values from .23 to .7 in 2023 and .3 to .63 in 2022.  This finding for lacrosse compares favorably to what Joyce et al. (4) found for D1 college soccer, where the total shots ratio ranged from .24 to .69 in D1 men’s soccer.

            The second measure of team success is attacking scoring efficiently or goals to shots ratio.

The average attaching scoring efficiency for 2023 and 2022 was .42. This measure had a relatively smaller variability than the total shots ratio, with a minimum of around .3 for both years and a maximum of .5 in 2023 to .58 in 2023. This maximum means that the teams with the highest attacking scoring efficiency earn an average of one goal for every two shots. Being able to convert shots into goals is an essential aspect of winning games. Lacrosse teams are much more likely to convert shots into goals, as Joyce et al. (4) found an average attacking scoring efficiency of .1 or 1 goal for every ten shots in D1 men’s soccer.

The third measure of team success is the defending scoring efficiency, which is contracted as

This final measure determines if teams can prevent opponents from turning shots into goals. The average values for defending scoring efficiency are slightly higher than attaching scoring efficiency, with an average of .43 in 2023 and .44 in 2022. The variability is higher for defending scoring efficiency than attacking scoring efficiency, with a minimum of .31 in 2023 and .34 in 2022 and a maximum of .66 in 2023 and .77 in 2022. Teams that are better at preventing shots from being converted into goals typically have a higher win percentage.

Regression Model The first step in our regression analysis is to empirically estimate the degree to which offensive and defensive statistics impact the win percentage for the 2023 lacrosse season. The win percentage regression model takes the form:

            The second part of the analysis follows Joyce et. al. (4) to determine what factors impact the change in win percentage between the 2023 and 2022 lacrosse seasons.  The regression model is as follows

where ε_i is the error term and i is the individual women’s lacrosse team. As with the individual season analysis, this model is estimated using ordinary linear regression and quantile regression at the 50th, 25th, and 75th percentiles.

Table 3 contains the results for the estimation of equation (4) from the 2023 lacrosse season with robust standard errors in parentheses. Looking first at the results from the ordinary least squares model, 86% of the variation in win percentage is explained by the 11 independent variables. Turning to the variables that are statistically significant, each additional goal results in an increase of .18 in win percentage, while each opponent’s goal results in a decrease of .2 in win percentage, with goals and opponent’s goals nearly offsetting each other. On average, one additional unassisted goal results in an increase of .13 in win percentage. Being a new D1 women’s lacrosse team in 2023 results in a 9 point marginally statistically significant decrease in win percentage relative to teams that have been in the league in previous years. This result suggests that new D1 teams have a difficult time navigating their first year likely due to players and coaches lacking experience and chemistry, making obtaining wins more difficult. Women’s lacrosse teams who participated in the 2023 NCAA Championship Tournament have a statistically significant almost 5 point higher win percentage than those who did not participate in the tournament. This finding is not surprising given that the two ways to get a team into the tournament are to either receive an automatic bid by winning their conference tournament or earn an at-large bid by having a compelling enough record during the regular season and conference playoffs.

The last three columns of table 3 contain quantile regression results at the 50th, 25th, and 75th percentiles of the win percentage distribution. Opponent’s goals are the only statistically significant factor to explain wins across all three percentiles. The magnitude of opponent’s goals is largest at the 25th percentile at -.24 and is -.20 for both the 50th and 75th percentile. Teams at the 25th and 50th percentiles of the win percentage distribution that participates in the NCAA end-of-season tournament has a statistically significant 7 point and 6 point higher win percentage, respectively, relative to those who did not participate, while this variable is not statistically significant at the 75th percentile. This may be because most, 73%, of the tournament participants come from the teams at the top 25% of the win percentage distribution, while most teams at the middle and bottom of the distribution did not participate in the tournament. Aside from this difference, the results are similar between the models at the three points in the win percentage distribution.

Table 4 contains the second part of the regression analysis which estimates equation (5) that attempts to determine what factors impact the change in win percentage between the 2023 and 2022 seasons. The variables contained in this analysis mimic those in Joyce et. al. (4) for men’s D1 college soccer. Looking at the OLS results, teams that had a one unit increase in the change in total shots ratio between the two seasons had a 2.4 increase in the change in win percentage. Teams with a 1 unit increase in the change in attacking efficiency had a 1 unit increase in the change in win percentage, and teams with a one unit increase in the change in defending efficiency decreased the change in win percentage by 1.2 points. The statistical significance between these lacrosse results and those found for soccer by Joslyn et al. (4) are identical, suggesting that even though there are many differences between the two sports, the same factors are important in explaining the change in win percentage between consecutive years. Comparing magnitudes between the two applications is not possible because the estimation methods differed. The statistical significance of the variables included in the quantile regression evaluated at the 50th, 25th, and 75th percentiles were the same as in the OLS regression. The quantile regression performed at the 25th percentile of the change in win percentage had the highest impact for the change in total shots ratio and the change in attacking efficiency, while the change in defending efficiency had the smallest impact. The change in total shots ratio and the change in attacking efficiency had the smallest impact for those teams at the 75th percentile, while the change in defending efficiency had the largest impact for those teams at the 50th percentile. These results suggest that the factors that impact the change in win percentage are similar across teams at the bottom and the top of the change in win percentage distribution, although the marginal impacts differed slightly between the percentiles.

It is not surprising that additional goals led to an increase in win percentage and an increase in opponent’s goals led to a decrease in win percentage. However, it was unanticipated that many of the other offensive and defensive statistics included in the regression were not statistically significant. It is likely that these other factors either lead to the team’s ability to score goals, such as shots, ground balls, and caused turnovers, or lead to the opponent’s goals, such as turnovers. One drawback of this research is that it does not investigate how these other factors impact goals and opponent’s goals. One adage in lacrosse is “win the draw, win the game.” Even though draw controls are not statistically significant in explaining win percentage, there was no information contained in the box scores on how many goals were obtained when the team won the draw control or how many goals were conceded when the team lost the draw control. More detailed information would be needed to investigate this relationship further. Other factors that likely explain win percentage and changes in win percentage such as team chemistry, the presence of a star player, the experience of the players and the coaches, and how different game management strategies, such as the usage of substitutes and quickness of play, are not included because they are difficult to measure, not included in the box scores, or both.

For a lacrosse coach or lacrosse player who is looking to improve win percentage between seasons, it is comforting to note that focusing on improving the changes in total shots ratio, attacking scoring efficiency, and becoming better at defending by decreasing the opponent’s goal-to-shot ratio will lead to an increase in the change in win percentage. One major drawback of this research is that it does not point to the factors that cause improvements in these variables and how they feed into additional goals or fewer conceded goals.

This study is the first to analyze which factors impact win percentage and changes in win percentage for NCAA D1 women’s lacrosse. The regression results suggest that goals, unassisted goals, and those who competed in the NCAA tournament had a positive impact on win percentage, while opponent’s goals and teams that were new in 2023 had a negative impact on win percentage. These factors were similar across the distribution of win percentage at the 25th, 50th, and 75th percentiles. Changes in win percentage between the 2023 and 2022 seasons are positively impacted by the change in the total shots ratio and attacking scoring efficiency and negatively impacted by the change in defending scoring efficiency. Even though there are many differences between lacrosse and soccer, the findings of this research and those of Joyce et. al. (4) that focus on college soccer suggest that the factors that explain changes in win percentage are similar between the two sports. These results also suggest that the statistics that explain win percentage and change in win percentage are similar between teams at the bottom, at the middle, and at the top of the distributions.

Women’s lacrosse programs at the collegiate level as well as at the national level can use these results to determine which factors to focus on when attempting to improve their win percentage within a specific year or over the course of several years. This research suggests that teams should emphasize their efforts in practice and in games on factors that increase goals as well as those factors that prevent goals. The lack of empirical analysis at the collegiate level, especially for women’s sports, can be rectified using available data. Additional publicly available information would make individual game analysis more informative such as how winning a draw control impacts goals as well as how focusing on specific factors such as caused turnovers or increasing assists increases goals and therefore positively impacts a team’s chances of winning.

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• (4) Joslyn, M.R., Joslyn, N. J. & Joslyn, M. R. (2017). What delivers an improved season in men’s college soccer? The relative effects of shots, attacking and defending scoring efficiency on year-to-year change in season win percentage. The Sport Journal, 24, 1-12.
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Perspective article, artificial intelligence and machine learning in sport research: an introduction for non-data scientists.

• Institute for Health and Sport, Victoria University, Melbourne, VIC, Australia

In the last two decades, artificial intelligence (AI) has transformed the way in which we consume and analyse sports. The role of AI in improving decision-making and forecasting in sports, amongst many other advantages, is rapidly expanding and gaining more attention in both the academic sector and the industry. Nonetheless, for many sports audiences, professionals and policy makers, who are not particularly au courant or experts in AI, the connexion between artificial intelligence and sports remains fuzzy. Likewise, for many, the motivations for adopting a machine learning (ML) paradigm in sports analytics are still either faint or unclear. In this perspective paper, we present a high-level, non-technical, overview of the machine learning paradigm that motivates its potential for enhancing sports (performance and business) analytics. We provide a summary of some relevant research literature on the areas in which artificial intelligence and machine learning have been applied to the sports industry and in sport research. Finally, we present some hypothetical scenarios of how AI and ML could shape the future of sports.

Introduction

It was in Moneyball ( Lewis, 2004 ), the famous success storey of the Major League Baseball team “Oakland Athletics,” that using in-game play statistics came under focus as a means to assemble an exceptional team. Despite Oakland Athletics' relatively small budget, the adoption of a rigorous data-driven approach to assemble a new team led to the playoffs in the year 2002. An economic evaluation of the Moneyball hypothesis ( Hakes and Sauer, 2006 ) describes how, at the time, a baseball hitters' salary was not truly explained by the contribution of a player's batting skills to winning games. Oakland Athletics gained a big advantage over their competitors by identifying and exploiting this information gap. It's been almost two decades since Moneyball principles, or SABRmetrics ( Lewis, 2004 ) was introduced to baseball. SABR stands for Society for American Baseball Research and SABRmetricians are those scientists who gather the in-game data and analyse it to answer questions that will lead to improving team performance. Since the success of the Oakland Athletics, most MLB teams started employing SABRmetricians. The ongoing and exponential increase of computer processing power has further accelerated the ability to analyse “big data,” and indeed, computers increasingly are taking charge of the deeper analysis of data sets, through means of artificial intelligence (AI). Likewise, the surge in high-quality data collection and data aggregation (accomplished by organisations like Baseball Savant/StatCast, ESPN and others) are key ingredients to the spike in the accuracy and breadth of analytics that was observed in the MLB in recent years.

The adoption of AI and statistical modelling in sports has become therefore more prominent in recent years as new technologies and research applications are impacting professional sports at various levels of sophistication. The wide applicability of machine learning algorithms, combined with increasing computing processing power as well as access to more and new sources of data in recent years, has made sports organisations hungry for new applications and strategies. The overriding aim is still to make them more competitive on and off the field–in athletic and business performance. The benefits of leveraging the power of AI can, in that regard, take different forms from optimising business or technical decision making to enhancing athlete/team performance but also increasing demand for attendance at sporting events, as well as promoting alternative entertainment formats of the sport.

We next list some areas where AI and machine learning (ML) have left their footprints in the world of sports ( Beal et al., 2019 ) and provide some examples of applications in each (some of the listed applications could overlap with one or more of the areas).

• Game activity/analytics: match outcome modelling, player/ball Tracking, match event (e.g., shot) classification, umpire assistance, sports betting .

• Talent identification and acquisition: player recruitment, player performance measurement, biomechanics .

• Training and coaching: assessment of team formation efficacy, tactical planning, player injury modelling .

• Fan and business focused: measurement of a player's economic value, modelling demand for event attendance, ticket pricing optimisation (variable and dynamic), wearable and sensor design, highlight packaging, virtual and augmented reality sport applications, etc .

The field of AI (particularly ML) offers new methodologies that have proven to be beneficial for tackling the above challenges. In this perspective paper we aim to provide sports business professionals and non-technical sports audiences, coaches, business leaders, policy makers and stakeholders with an overview of the range of AI approaches used to analyse sport performance and business centric problems. We also discuss perspectives on how AI could shape the future of sports in the next few years.

Research on AI and ML in Sports

In this section, we will not be reviewing examples of how AI has been applied to sports for a specific application, but rather, we will look at the intersection of AI and sports at a more abstract level, discussing some research that either surveyed or summarised the application of AI and ML in sports.

One of the earliest works discussing the potential applications of artificial intelligence in sports performance, and its positive impact on improving decision-making is by Lapham and Bartlett (1995) . The paper discusses how expert systems (i.e., a knowledge-based database used for reasoning) can be used for sports biomechanics purposes. Bartlett (2006) reviewed developments in the use of AI in sports biomechanics (e.g., throwing, shot putting, football kicking, …) to show that, at the time of writing, expert systems were marginally used in sports biomechanics despite being popular for “gait analysis” whereas Artificial Neural Networks were used for applications such as performance patterns in training and movement patterns of sports performers. An Artificial Neural Network (ANN) is a system that mimics the functionality of a human brain. ANNs are used to solve computational problems or estimate functions from a given data input, by imitating the way neurons are fired or activated in the human brain. Several (layers of) artificial neurons, known as perceptrons, are connected to perform computations which return an output as a function of the provided input ( Anderson, 1995 ).

Bartlett (2006) predicted that multi-layer ANNs will play a big role in sports technique analysis in the future. Indeed, as we discuss later, multi-layer ANNs, now commonly referred to as Deep Learning, have become one of the most popular techniques in sports related analytics. Last but not least Bartlett (2006) described the applications of Evolutionary Computation and hybrid systems in the optimization of sports techniques and skill learning. Further discussion around the applications of AI in sports biomechanics can be found in Ratiu et al. (2010) . McCabe and Trevathan (2008) discussed the use of artificial intelligence for prediction of sporting outcomes, showing how the behaviour of teams can be modelled in different sporting contests using multi-layer ANNs.

Between 2006 and 2010, machine learning algorithms, particularly ANNs were becoming more popular amongst computer scientists. This was aided by the impressive improvements in computer hardware, but also due to a shift in mindset in the AI community. Large volumes of data were made public amongst researchers and scientists (e.g., ImageNet a visual database delivered by Stanford University), and new open-source machine learning competitions were organised (such as Netflix Prize and Kaggle). It is these types of events that have shaped the adoption of AI and machine learning in many different fields of study from medicine to econometrics and sports, by facilitating access to training data and offering free open-source tools and frameworks for leveraging the power of AI. Note that, in addition to ANN, other machine learning techniques are utilised in such competitions, and sometimes these can be used in combination with one another. For instance, some of the techniques that went into the winning of the Netflix prize include singular value decomposition combined with restricted Boltzmann machines and gradient boosted decision trees.

Other examples discussing ANNs in sports include Novatchkov and Baca (2013) who discuss how ANNs can be used for understanding the quality of execution, assisting athletes and coaches, and training optimisation. However, the applications of AI to sports analytics go beyond the use of ANNs. For example, Fister et al. (2015 ) discussed how nature-inspired AI algorithms can be used to investigate unsolved research problems regarding safe and effective training plans. Their approach ( Fister et al., 2015 ) relies on the notion of artificial collective intelligence ( Chmait et al., 2016 ; Chmait, 2017 ) and the adaptability of algorithms to adapt to a changing environment. The authors show how such algorithms can be used to develop an artificial trainer to recommend athletes with an informed training strategy after taking into consideration various factors related to the athlete's physique and readiness. Other types of scientific methods that include Bayesian approaches have been applied to determining player abilities ( Whitaker et al., 2021 ) but also predicting match outcomes ( Yang and Swartz, 2004 ). Bayesian analysis and learning is an approach for building (statistical and inference) models by updating the probability for a hypothesis as more evidence or information becomes available by using Bayes' theorem ( Ghosh et al., 2007 ).

There are numerous research papers in which AI and ML is applied to sport, and it is not our aim to comprehensively discuss these works here 1 . However, we refer to a recent survey that elaborates on this topic. Beal et al. (2019) surveyed the applications of AI in team sports. The authors summarised existing academic work, in a range of sports, tackling issues such as match outcome modelling, in-game tactical decision making, player performance in fantasy sport games, and managing professional players' sport injuries. Work by Nadikattu (2020) presents, at an abstract level, discussions on how AI can be implemented in (American) sports from enhancing player performance, to assisting coaches to come up with the right formations and tactics, to developing automated video highlights of sports matches and supporting referees using computer vision applications.

We emphasise that the application of AI in sports is not limited to topics of sports performance, athlete talent identification or the technical analysis of the game. The (off the field) business side of sports organisations is rapidly shifting towards a data driven culture led by developing profiles of their fans and their consumer preferences. As fans call for superior content and entertainment, sport organisations must react by delivering a customised experience to their patrons. This is often achieved by the use of statistical modelling as well as other machine learning solutions, for example, to understand the value of players from an economic perspective. As shown in Chmait et al. (2020a) , investigating the relationship between the talent and success of athletes (to determine the existence of what is referred to as superstardom phenomenon or star power) is becoming an important angle to explore value created in sport. To provide an idea of the extent of such work, we note some sports in which the relationship between famous players/teams and their effect on audience attendance or sport consumption has been studied:

• In soccer ( Brandes et al., 2008 ; Jewell, 2017 ),

• In Major League Baseball ( Ormiston, 2014 ; Lewis and Yoon, 2016 )

• In the National Basketball Association ( Berri et al., 2004 ; Jane, 2016 )

• In tennis: superstar player effect in demand for tennis tournament attendance ( Chmait et al., 2020a ), the presence of a stardom effect in social media ( Chmait et al., 2020b ), player effect on German television audience demand for live broadcast tennis matches ( Konjer et al., 2017 )

• And similarly, in Cricket ( Paton and Cooke, 2005 ), Hockey ( Coates and Humphreys, 2012 ), and in the Australian Football League ( Lenten, 2012 ).

AI algorithms are being used in Formula 1 (F1) to improve the racing tactics of competing teams by analysing data from hundreds of sensors in the F1 car. Recent work by Piccinotti (2021) shows how artificial intelligence can provide F1 with automated ways for identifying tyre replacement strategies by modelling pit-stop timing and frequency as sequential decision-making problems.

Researchers from Tennis Australia and Victoria University devised a racket recommendation technique based on real HawkEye (computer vision system) data. An algorithm was used to recommend a selection of rackets based on movement, hitting pattern and style of the player with the aim to improve the player's performance ( Krause, 2019 ).

Accurate and fair judging of sophisticated skills in sports like gymnastics is a difficult task. Recently, a judging system was developed by Fujitsu Ltd. The system scores a routine based on the angles of a gymnast's joints. It uses AI to analyse 3D laser sensors that capture the gymnasts' movements ( Atiković et al., 2020 ).

Finally, it is important to note the exceptionally successful adoption of AI in board games like Chess, Checkers, Shogi and the Chinese game of GO, as well as virtual games (like Dota2 and StarCraft). In the last couple of decades, AI has delivered a staggering rise in performance in such areas to the point that machines (almost) constantly defeat human world champions. We refer to some notable solutions like Schaeffer et al. (2007) Checkers artificial algorithm, DeepBlue defeating Kasparov in Chess ( Campbell et al., 2002 ), AlphaGo Zero defeating Lee Sedol in Go ( Silver et al., 2017 ) (noting that AlphaZero is also unbeatable in chess) and Vinyals et al. (2019) AlphaStar in StarcraftII as well as superhuman AI for multiplayer poker ( Brown and Sandholm, 2019 ). Commonly, in these types of games or sports, AI algorithms rely on a Reinforcement Learning approach (which we will describe later) as well as using techniques like the Monte-Carlo Search Trees to explore the game and devise robust strategies to solve and play these games. Some of the recent testbeds used to evaluate AI agents and algorithms are discussed in Hernández-Orallo et al. (2017 ). For a broader investigation of AI in board and virtual/computer games refer to Risi and Preuss (2020) .

The rise of applying AI and ML is unstoppable and to that end, one might be wondering how AI an ML tools work and why are they different from traditional summary analytics. We touch upon these considerations in the next section.

The Machine Learning Paradigm

To understand why ML is used in a wide range of applications, we need to take a look into the difference between recent AI approaches to learning and traditional analytics approaches. At a higher conceptual level, one can describe old or traditional approaches to sports analytics, as starting off with some set of rules that constitute the problem definition, some data that is to be processed using a program/application which will then deliver answers to the given problem. In contrast, in a machine learning/predictive analytics paradigm, the way this process works is fundamentally different. For instance, in some approaches of the ML paradigm, one typically starts by feeding the program with answers and corresponding data to a specific problem, with an algorithm narrowing down the rules of the problem. These rules are later used for making predictions and they are evaluated or validated by testing their accuracy over new (unseen) data.

To that end, machine learning is an area of AI that is concerned with algorithms that learn from data by performing some form of inductive learning. In simple terms, ML prediction could be described as a function 2 from a set of inputs i 1 , i 2 , …, i n , to forecast an unknown value y , as follows f ( w 1 * i 1 , w 2 * i 2 , …, w n * i n ) = y , where w t is the weight of input t .

Different types or approaches of ML are used for different types of problems. Some of the most popular are supervised learning, unsupervised learning , and reinforcement learning :

• In supervised learning, we begin by observing and recording both inputs (the i 's) and outputs (the y 's) of a system, for a given period of time. This data (collection of correct examples of inputs and their corresponding outputs) is then analysed to derive the rules that underly the dynamics of the observed system, i.e., the rules that map a given input to its correct output.

• Unlike the above, in unsupervised learning, the correct examples or outputs from a given system are not available. The task of the algorithm is to discover (previously unnoticed) patterns in the input data.

• In reinforcement learning, an algorithm (usually referred to as an agent) is designed to take a series of actions that maximise its cumulative payoff or rewards over time. The agent then builds a policy (a map of action selection rules) that return a probability of taking a given action under different conditions of the problem.

For a thorough introduction to the fundamentals of machine learning and the popular ML algorithms see Bonaccorso (2017) . The majority of AI applications in sports are based on one or more of the above approaches to ML. In fact, in most predictive modelling applications, the nature of the output y that needs to be predicted or analysed could influence the architecture of the learning algorithm.

Explaining the details of how different ML techniques work is outside the scope of this paper. However, to provide an insight into how such algorithms function in layman's terms and the differences between them, we briefly present (hypothetical) supervised, unsupervised and reinforcement learning problems in the context of sports. These examples will assist the professionals but also applied researchers who work in sport to better understand the way that data scientists think so to facilitate talking to them about their approach and methodology, without requiring to dive deep into the details of the underlying analytics.

Supervised Learning: Predicting Player Injury

Many sports injuries (e.g., muscle strain) can be effectively treated or prevented if one is able to detect them early or predict the likelihood of sustaining them. There could be many different (combinations of) reasons/actions leading to injuries like muscle strain. For example, in the Australian Football League, some of hypotheses put forward leading to muscle strain include: muscle weakness and lack of flexibility, fatigue, inadequate warm-up, and poor lumbar posture ( Brockett et al., 2004 ). Detecting the patterns that can lead to such injuries is extremely important both for the safety of the players, and for the success and competitiveness of the team.

In a supervised learning scenario, data about the players would be collected from previous seasons including details such as the number of overall matches and consecutive matches they played, total time played in each match, categorised by age, number of metres run, whether or not they warmed up before the match, how many times they were tackled by other players, and so on , but more importantly, whether or not the players ended up injured and missed their next match.

The last point is very important as it is the principal difference between supervised learning and other approaches: the outcome (whether or not the player was injured) is known in the historical data that was collected from previous seasons. This historical data is then fed (with the outcome) to a machine learning algorithm with the objective of learning the patterns (combination of factors) which led to an injury (and usually assigning a probability of the likelihood of an injury given these patterns). Once these patterns are learnt, the algorithm or model is then tested on new (unseen data) to see if it performs well and indeed predicts/explains injury at a high level of accuracy (e.g., 70% of the time). If the accuracy of the model is not as required, the model is tuned (or trained with slightly different parameters) until it reaches the desired or acceptable accuracy. Note here that we did not single out a specific algorithm or technique to achieve the above. Indeed, this approach can be applied using many different ML algorithms such as Neural Networks, Decision Trees and regression models.

Unsupervised Learning: Fan Segmentation

We will use a sport business example to introduce the unsupervised learning approach. Most sports organisations keep track of historical data about their patrons who attended their sporting events, recording characteristics such as their gender, postcode, age, nationality, education, income, marital status, etc. A natural question of interest here is to understand if the different segments of customers/patrons will purchase different categories (e.g., price, duration, class etc.) of tickets.

Some AI algorithms are designed to help split the available data, so that each data point (historical ticket sale) sits in a group/class that is similar to the other data points (other sales) in that same class given the recorded features. The algorithm will then use some sort of a similarity or distance metric to classify the patrons according to the category of tickets that they might purchase.

This is different from how supervised learning algorithms, like those discussed in the previous section, work. As we described before, in supervised learning we instruct the algorithm with the outcome in advance while training it (i.e., we classify/label each observation based on the outcome: injury or no injury, cheap or expensive seats, …). In the unsupervised learning approach, there is no such labelling or classification of existing historical data. It is the mission of the unsupervised learning algorithm to discover (previously unnoticed) patterns in the input data and group it into (two or more) classes.

Imagine the following use case where an Australian Football League club aims to identify a highly profitable customer segment within its entire set of stadium attendees, with the aim to enhance its marketing operations. Mathematical models can be used to discover (segments of) similar customers based on variations in some customer attributes within and across each segment. A popular unsupervised learning algorithm to achieve such goal is the K-means clustering algorithm which finds the class labels from the data. This is done by iteratively assigning the data points (e.g., customers) from the input into a group/class based on the characteristics of this input. The essence is that the groups or classes to which the data points are assigned to are not defined prior to exploring the input data (although the number of groups or segments can be pre-defined) but are rather dynamically formed as the K-means algorithm iterates over the data points. In the context of customer segmentation, when presenting the mathematical model (K-means algorithm) with customer data, there is no requirement to label a portion (or any of) of this data into groups in advance in order to train the model as usually done in supervised models.

Reinforcement Learning: Simulations and Fantasy Sports

As mentioned before, in reinforcement learning, an algorithm (such as Q-learning and SARSA algorithms) learns how to complete a series of tasks (i.e., solve a problem) by interacting with an (artificial) environment that was designed to simulate the real environment/problem at hand. Unlike the case with supervised learning, the algorithm is not explicitly instructed about the right/accurate action in different states/conditions of the environment (or steps of problem it is trying to solve). But rather it incrementally learns such a protocol through reward maximisation.

In simple terms, reinforcement learning approaches represent problems using what are referred to as: an agent (a software algorithm), and a table of states and actions . When the agent executes an action, it transitions from one state to another and it receives a reward or a penalty (a positive or negative numerical score respectively) as a result. The reward/penalty associated with the action-state combination is then stored in the agent's table for future reference and refinement. The agent's goal is to take the action that maximises its reward. When the agent is still unaware of the expected rewards from executing a given action when at a given state, it takes a random action and updates its table following that action. After many (thousands of) iterations over the problem space, the agent's table holds (a weighted sum of) the expected values of the rewards of all future actions starting from the initial state.

Reinforcement learning has been applied to improve the selection of team formations in fantasy sports ( Matthews et al., 2012 ). Likewise, the use of reinforcement learning is prominent in online AI bots and simulators like chess, checkers, Go, poker, StarCraft, etc.

Finally, it is important to also note the existence of genetic or evolutionary algorithms, sometimes referred to as nature/bio-inspired algorithms. While such algorithms are not typically considered to be ML algorithms (but rather search techniques and heuristics), they are very popular in solving similar types of problems tackled by ML algorithms. In short, the idea behind such algorithms is to run (parallel) search, selection and mutation techniques, by going over possible candidate solutions of a problem. The solutions are gradually optimised until reaching a local (sub-optimal) or global maximum (optimal solution). To provide a high-level understanding of evolutionary algorithms, consider the following sequence of steps:

• We start by creating (a population of) initial candidate or random strategies/solutions to the problem at hand.

• We assess these candidate solutions (using a fitness function) and assign scores to each according to how well they solve the problem at hand.

• We then pick a selection of these candidate solutions that performed best at stage two above. We then combine ( crossbreed ) these together to generate ( breed) new solutions (e.g., take some attributes from one candidate solution and others from another candidate solution in order to come up with a new solution).

• We then apply random changes ( mutations ) to the resulting solutions from the previous step.

• We repeat the solution combination/crossbreeding process until a satisfactory solution is reached.

Evolutionary algorithms can be used as alternative means for training machine learning algorithms such as reinforcement learning algorithms and deep neural networks.

The Future of AI in Sport

There is no doubt that AI will continue to transform sports, and the ways in which we play, watch and analyse sports will be innovative and unexpected. In fact, machine learning has drastically changed the way we think about match strategies, player performance analytics but also how we track, identify and learn about sport consumers. A Pandora's box of ethical issues is emerging and will increasingly need to be considered when machines invade the traditionally human centred and naturally talented athlete base of sport. It is unlikely that AI will completely replace coaches and human experts, but there is no doubt that leveraging the power of AI will provide coaches and players with a big advantage and lead over those who only rely on human expertise. It will also provide sport business managers with deeper, real time insights into the behaviours, needs and wants of sport consumers and in turn AI will become a main producer of sport content that is personalised and custom made for individual consumers. But human direction and intervention seems to be, at least in the near future, still essential working towards elite sport performance and strategic decision making in sport business. The sporting performance on the field is often produced as an entertainment spectacle, where the sporting context is the platform for generating the business of sport. Replacing referees with automated AI is clearly possible and increasingly adopted in various sports, because it is more accurate and efficient, but is it what the fans want?

What might the future of sport with increasingly integrated AI look like? Currently, most of the research in AI and sports is specialised. That is to provide performance or business solutions and solve specific on and off field problems. For instance, scientists have successfully devised solutions to tackle problems like player performance measurement, and quantifying the effect of a player/team on demand for gate attendance. Nevertheless, our research has not identified studies (yet) that provide a 360-degree analysis on, for example, the absolute value of an athlete by taking into account all the dimensions of his or her performance on how much business can be developed, for example in regard to ticket sales or endorsement deals.

One of the main challenges to achieve such a comprehensive analysis is mainly due to the fact that data about players and teams, and commercial data such as ticket sales and attendance numbers, are kept proprietary and are not made public to avoid providing other parties with competitive information. Moreover, privacy is an important consideration as well. Regulations about data privacy and leakage of personal identification details must be put in place to govern the use and sharing of sports (performance and consumption) data. Data ownership, protection, security, privacy and access will all drive the need for comprehensive and tight legislation and regulation that will strongly influence the speed and comprehensiveness of the adoption of AI in sport. To that end, it is worth considering privacy and confidentiality implications independently when studying the leagues' journey of AI adoption compared to that of individual teams and ultimately the individual players. Eventually, the successful adoption of AI in a sports league will likely depend on the teams in that league and their players to be willing to share proprietary data or insights with other teams in the league. Performance data of players in particular is becoming a hot topic of disputation. It may well be AI that will determine the bargaining power of players and their agents in regard to the value of their contracts. As an extension of this it will then also be AI providing the information that will determine if players are achieving the performance objectives set by coaches and as agreed to in contracts. In other words, confidentiality and ownership of league, team or player level data will become an increasing bone of legal contention and this will be reflected in the complexity of contractual agreements and possible disputes in the change rooms and on the field of play. Being in control of which data can or cannot, and will or will not, be used is at stake.

From an economic perspective, relying on artificial algorithms could increase the revenue of sports organisations and event organisers when enabled to apply efficient variable and dynamic pricing strategies and build comprehensive and deep knowledge consumer platforms. Different types of ML algorithms can be adopted to deliver more effective customer marketing via personalisation and to increase sales funnel conversion rates.

Finally, for a window on the future of data privacy, it might be useful to return to baseball where the addiction to big data started its spread across the high-performance sport industry. Hattery (2017 , p. 282) explains that in baseball “using advanced data collection systems … the MLB teams compete to create the most precise injury prediction models possible in order to protect and optimise the use of their player-assets. While this technology has the potential to offer tremendous value to both team and player, it comes with a potential conflict of interest. Players' goals are not always congruent with those of the organisation: the player strives to protect his own career while the team is attempting to capitalise on the value of an asset. For this reason, the player has an interest in accessing data that analyses his potential injury risk. This highlights a greater problem in big data: what rights will individuals possess regarding their own data points?”

This privacy issue can be further extended to the sport business space Dezfouli et al. (2020) have shown how AI can be designed to manipulate human behaviour. Algorithms learned from humans' responses who were participating in controlled experiments. The algorithms identified and targeted vulnerabilities in human decision-making. The AI succeeded in steering participants towards executing particular actions. So, will AI one day be shaping the spending behaviour of sports fans by exploiting their fan infused emotional vulnerabilities and monitoring their (for example) gambling inclinations? Will AI sacrifice the health of some athletes in favour of the bigger team winning the premiership? Or is this already happening? Time will tell.

Data Availability Statement

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.

Author Contributions

NC and HW had major contribution to the writing of this manuscript. NC contributed to the writing of the parts around artificial intelligence and machine learning and provided examples of these. HW shaped the scope of the manuscript and wrote and edited many of its sections particularly the introduction and the discussion. Both authors contributed to the article and approved the submitted version.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: artificial intelligence, machine learning, sports business, sports analytics, sport research, future of sports

Citation: Chmait N and Westerbeek H (2021) Artificial Intelligence and Machine Learning in Sport Research: An Introduction for Non-data Scientists. Front. Sports Act. Living 3:682287. doi: 10.3389/fspor.2021.682287

Received: 18 March 2021; Accepted: 15 November 2021; Published: 08 December 2021.

Reviewed by:

Copyright © 2021 Chmait and Westerbeek. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Nader Chmait, nader.chmait@vu.edu.au

This article is part of the Research Topic

The Future of Sport Business