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Original research article, a goal scoring probability model for shots based on synchronized positional and event data in football (soccer).

• 1 Sportec Solutions AG, Subsidiary of the Deutsche Fußball Liga (DFL), Munich, Germany
• 2 Institute of Sports Science, University of Tübingen, Tübingen, Germany
• 3 DFB-Akademie, Deutscher Fußball-Bund e.V., Frankfurt am Main, Germany

Due to the low scoring nature of football (soccer), shots are often used as a proxy to evaluate team and player performances. However, not all shots are created equally and their quality differs significantly depending on the situation. The aim of this study is to objectively quantify the quality of any given shot by introducing a so-called expected goals (xG) model. This model is validated statistically and with professional match analysts. The best performing model uses an extreme gradient boosting algorithm and is based on hand-crafted features from synchronized positional and event data of 105, 627 shots in the German Bundesliga. With a ranked probability score (RPS) of 0.197, it is more accurate than any previously published expected goals model. This approach allows us to assess team and player performances far more accurately than is possible with traditional metrics by focusing on process rather than results.

1. Introduction

In professional football (soccer), only 1% of all attacking plays and only around 10% of all shots taken end up in a goal ( Pollard and Reep, 1997 ; Tenga et al., 2010 ; Lucey et al., 2014 ). However, goals alone decide the outcome of a game and are the most common metric to judge both a team's and individual player's performance. For example, both the best goal scorers 1 and the players with the most assists 2 receive a lot of attention from experts and the media. Nevertheless, judging performances solely based on this binary metric ( goal or no goal ) loses a lot of information and places results over process. For example, the performance from an outstanding creative player could be made void by strikers missing all their chances.

For this reason, in football as well as in other sports, it has become typical to consider more granular process-based metrics. In baseball, scouts and experts focused their attention on homeruns or hits for decades until more complex evaluation metrics changed the assessment procedure of hitters' performance significantly ( James, 1985 ). Another famous example is basketball: By calculating scoring probabilities of different shot locations ( Reich et al., 2006 ; Chang et al., 2014 ; Harmon et al., 2016 ; Jagacinski et al., 2019 ), the NBA's shooting behavior changed significantly 3 . The high scoring nature of basketball enables clubs to go even further and to apply individual shooting efficiency models ( Beshai, 2014 ). Similar shot prediction models were also developed for ice hockey ( Macdonald, 2012 ) as well as for return plays in tennis ( Wei et al., 2016 ) and table tennis ( Draschkowitz et al., 2015 ).

The fact that football is the lowest scoring game of the above-mentioned sports, makes it harder to develop such models, because of the scarcity of data. Consequently, the rareness and therefore importance of goals makes such a metric even more relevant when assessing teams and players. As another consequence of this low-scoring nature, the role of shots as a success proxy within several studies in football is fortified ( Spearman et al., 2017 ). However, assessing shots just by being successful or not is a too rough abstraction that warps reality. An expected goals model (hereafter xG model ) tries to estimate the probability of any given shot being converted to a goal based on various different factors describing the shot. These probabilities can then be added up per team and yield a “result-agnostic” description of the teams' performance. The xG metric is well-established in the football analytics community (see Davis and Robberechts, 2020 ) 4 , 5 , 6 , 7 . Although to the best of our knowledge, no peer-reviewed journal publication has introduced a positional data-driven xG model, valuable work has been done in “gray literature” like master theses ( Hedar, 2020 ; Rowlinson, 2020 ) and conference proceedings ( Lucey et al., 2014 ). Rathke (2017) analyzed in total around 18, 000 shots from one season of Bundesliga and Premier League based on manually acquired shot annotations. Differentiating between four different shooting types (open play footed shot, header, freekick, or penalty shot) , Ruiz et al. (2017) built a multi-layer perceptron to predict shot outcomes based on roughly 10, 000 shots. Using a similar approach, Fairchild et al. (2018) tried to predict the goal scoring probabilities of 1, 115 non-penalty shots from 99 Major League Soccer matches, again solely based on event data.

Recent developments in technology allows us not only to make use of manually annotated event data (shots, passes, goals with a manually assigned location) but also accurate positions of all 22 players and the ball at up to 25 times a second. It is quite intuitive that the positioning of the defensive team, especially of the goalkeeper, has a crucial influence on the shot outcome ( Lucey et al., 2014 ; Schulze et al., 2018 ). Figure 1 displays the positioning of relevant players during two shots occurring at similar spots. In the left figure, both a defender and the goalkeeper are in good position to block the shot, while in the right figure the attacker has already dribbled past the goalkeeper (#38) and defenders, and faces an easy tap-in into an empty goal 8 . However, this information is not covered in event data and thus not taken into consideration in the previously listed xG models. Lucey et al. (2014) were the first to estimate goal probabilities using event and positional data in their model. They used 10, 000 shots of the English Premier League.

Figure 1 . Player positions of two shots from roughly the same location, but different surrounding environments. In both cases, the blue team is playing from left to right.

In this paper, we will introduce a shot prediction model, utilizing event and positional data. The accuracy of this model is evaluated both statistically and based on the discussion with professional match analysts. We also incorporate their expertise both when defining the model's features and when interpreting their influence on the prediction. Additionally, we show how our model can support coaching staffs by introducing various use cases and applying them on one season worth of Bundesliga data.

The remainder of this paper is structured as follows. In section 2, we introduce the data and definitions. How event and tracking data are synchronized is described in section 3. Section 4 describes how the supervised prediction model is build, and finally, section 5 consists of two parts: practical applications (5.1) of our approach based on a season of German Bundesliga and a critical discussion of the results (5.2).

2. Data and Definitions

Like in most other professional football competitions, the German Bundesliga systematically collects positional and event data on a league-wide level in a pre-defined and thus consistent format. Positional data —often also referred to as tracking or movement data ( Stein et al., 2015 )—provides the positions of all players, referees, and the ball related to the pitch boundaries with a frequency of 25 Hz. These data are gathered by an optical tracking system, which captures high-resolution video footage from different camera perspectives. On the other hand, event data are manually acquired by trained operators live during the match. Among other things, this event data contain many details about basic events, such as passes, shots, fouls, saves, and so on including the involved players or special characteristics.

Since shots are an important statistic in football, the event data in the Bundesliga describe them with more than 20 attributes. For example, the collector differentiates between three basic shot types (leg, header, other) or six different scenarios how a player controlled the ball before taking a shot (direct, volley, two touches, dribbling > 10 m, dribbling < 10 m, set-piece) .

In this investigation, we make use of 105, 627 shots from German Bundesliga and 2 nd Bundesliga of the seasons 2013/2014 until 2019/2020. The event data were collected according to the official Bundesliga match-data catalog 9 , and the optical tracking data were provided by Chyronhego's TRACAB system 10 .

Due to a growing availability of optical tracking systems in football, several studies have been conducted to evaluate their accuracy ( Redwood-Brown et al., 2012 ; Linke et al., 2018 , 2020 ; Linke, 2019 ; Taberner et al., 2019 ). In Linke et al. (2020) , the two versions of the TRACAB system ( Gen 4 / Gen 5 ) 11 were compared to an accurate ground truth measurement 12 . Both systems achieved a diversion of < 10 cm from the ground truth system (RMSE Gen 4: 0.09 cm, Gen 5: 0.08 cm). A non-peer reviewed study confirmed these results 13 . All above-mentioned evaluation studies focused on player detection, whereas the detection of the ball—probably the hardest challenge for optical tracking systems—is not covered.

To the best of our knowledge, no scientific study evaluated the quality of event data. However, in the German Bundesliga the acquisition follows an elaborate quality assurance process. Critical information is double-checked manually live (e.g., goals and red cards). Finally, an independent person inspects and adds additional information (e.g., event locations) to all acquired event data after the match.

3. Making Use of Both Positional and Event Data

3.1. synchronizing shots with tracking data.

A major challenge when attempting to use both tracking and event data is that they are generally not aligned. This is due to the fact that they come from different data providers and/or acquisition methods, one specialized in logging events manually according to catalog of set definitions (i.e., what is considered a shot or a tackling) and the other focusing on extracting player positions through, for example, computer vision algorithms. This leads to two potential issues when synchronizing the data:

(a) The manual collected event time stamps are prone to human errors, e.g., reaction time, distractions, and decision time, leading to time offsets of up to 20 s based on our investigations.

(b) The two systems use their own clock, causing systematic offsets between the two sources.

For these reasons, a “naive” synchronization—using the time stamp from the event data—to identify player positions at the time of an event leads to large inaccuracies. The upper plots in Figure 2 display the coordinates of the players and the ball at the different moments of the scenario from Figure 1 (right plot). The scene describes Kevin Volland's (Bayer Leverkusen) 1:0 against Borussia Dortmund (BVB) at the 14th matchday in the 2017/2018 season: 14 The upper right plot in Figure 2 displays the shot time stamp tagged in the event data, which is roughly 2 s after the time stamp our synchronization suggests the shot took place (upper middle plot). The upper left plot in Figure 2 shows the positioning of the players 2 s prior to that. As one can see, the situations are drastically different ranging from a distant dribble to a player celebrating his goal. The figure underpins that a shift of a few seconds in the synchronization can have a massive impact on the features used for the xG calculation, like the shot location or the goalkeeper position.

Figure 2 . Relevant metrics for the synchronization over time. The green points highlight the time interval where we detect a potential individual ball possession sequence. The orange point indicates where the shot event was finally detected.

Therefore, we developed a synchronization algorithm tackling both issues. As a first step, we shift all tracking time stamps by the time difference between the kick-offs in both data sets. This resolves issue (b) and furthermore reduces a potential systematic delay in the manual event collection. In order to tackle issue (a), we compute several features that help to determine when a particular shot could have happened in the tracking data. First, we determine when the shooting player was in ball possession. We define potential individual ball possession sequences as the time interval when the player is in close proximity to the ball—our subject experts suggested 2 m as a cut-off, which is in line with Linke et al. (2018) . Next, within each possession window, we identify the frame with the maximum acceleration of the Euclidean distance between player and ball. This aims to identify the exact moment where a shot occurred. Lastly, since there are potentially many situations that fulfill the above-mentioned criteria, we identify which best matches the event description. For that we compute Euclidean distances between the player and ball, the player and the manual collected event location as well as between the ball and the manual collected event location. Additionally, we compute the time difference between the (shifted) tracking time stamps and the manual collected event time stamp. We compute a weighted sum of these features, and the one frame out of the solution space that minimizes this weighted sum is chosen. The weights were obtained by performing a grid-based search that aimed to optimize accuracy of the synchronization on a manual labeled test set. The lower part of Figure 2 shows how these features behave in the 20 s before and after the exemplary shot described above. When we applied this synchronization algorithm on the full data set of six seasons, the event shot times had an average absolute offset of 2.3 s (≈57 frames) from the synchronized frame. Figure 3 displays histograms of the differences in timing (left) and locations (right) of each shot.

Figure 3 . Time stamp (left) and shot location (right) differences between event and synchronized time stamps.

3.2. Evaluation of the Synchronization

In order to evaluate the accuracy of the synchronization, we manually annotated the timing of total 219 shots of the nine matches from matchday one of Bundesliga season 2018/2019. First, a full 90 min video animation of the 2D tracking data was created for each match. As a ground truth, we used a tactical video feed, which is filmed manually with an angle to capture all outfield player (and the most relevant goalkeeper). Additionally, for each match a xml-file 15 containing all shot-events, and the kick-off was produced. Next, we used the kick-offs in all three data sources to synchronize them manually as accurately as possible using Hudl Sportscode 16 —a dedicated tool for football video analysis with functionalities to combine different video sources and data sources (i.e., event data can be imported via xml-files). For each shot, we stop the video at the exact moment the shot occurred—defined as the first frame when the ball left the shooter—and extract this time point using Sportscode functionalities.

We now use these labeled shot timestamps as the ground truth and compare them with both, the results from our synchronization, and the event timestamps. Our synchronization displays an average absolute offset of 0.23 (±0.49) s, while the event timestamps differ by 1.82 (±4.06) s. Out of the 219 shots, we were able to synchronize 218, and 210 (95.9%) of these shots were < 0.3 s apart from the ground truth 17 . In contrast, only 63 (28.8%) of the event timestamps were within 0.3 s of the ground truth. It is evident that generally this synchronization is far superior to event timestamps. Two exemplary situations for a successful and an unsuccessful shot synchronization can be found here 18 , 19 .

When a shot cannot be synchronized, it is typically due to either tracking data quality issues (e.g., the ball is poorly tracked, and never gets close to the player taking the shot, or two players were swapped in the tracking data) or event data quality issues (e.g., the wrong shooter is identified). To ensure that the quality of the input data is as high as possible, all shots that could not be synchronized at all were excluded from further analysis. Over the entire data set, this was the case in 3.4% of the shots.

All together, the synchronization of positional and event data presents a tremendous improvement for the analysis of shots, and could potentially be extended, using a similar algorithm, to other event types, like passes or tacklings. As we have seen above, misidentifying the shot time just slightly can cause a stark misrepresentation of its surrounding circumstances, and consequently affect the xG value significantly.

4. Expected Goals Modeling

4.1. hand-crafted feature extraction.

To feed the supervised machine learning model, features influencing the goal scoring opportunity were defined together with professional match analysts from Bundesliga clubs and the German national team. A description of all features can be found in Table 1 . In order to make full use of the synchronization of our two data sources, the features are based on both event and tracking data. The goalkeeper positioning is included in two features: We check whether they are in the line of shot, defined as the triangle between the shot location and the two posts, which is also the baseline for our shot angle calculation. Second, the distance between the goalkeeper and the goal is used as features in our model. The defending players' positions, either threatening to block the shot or applying pressure on the shooter, are also taken into consideration. Similarly to the goalkeeper feature, we count the number of defenders in the line of shot. Based on the logic from Andrienko et al. (2017) , we calculate the total amount of pressure on the shooter aggregated over all defending players, as well as the maximum individual pressure on the shot-taking player. For both pressure metrics, we additionally compute the differences to the expected pressures given the shot location. Furthermore, the speed of the shooter, while taking the shot, is integrated in our model.

Table 1 . Features derived from synchronized positional and event data used to train our model.

4.2. Predict the Scoring Probability as a Supervised Machine Learning Task

For a total of 105, 627 shots, all features from Table 1 were calculated based on the synchronized positional and event data. Since the features shot type and freekick significantly influence the contribution of all other features, we split our problem into three subtasks: the prediction of goal scoring probabilities of open play leg-shots, headers, and direct freekicks. Per subtask, the optimal set of features was explored. Consequently, for all three subtasks we trained several supervised machine learning models based on 81, 462 open play leg-shots, 18, 748 headers and 5, 417 direct freekicks, respectively, labeled by the information whether the shot ended up in a goal (1) or not (0). For each subtask, the shots were randomly split into 60% training, 20% validation, and 20% test data sets. To avoid over representing teams or scores, this split was conducted for every match separately. The final model, shown in Table 1 (row 5), describes the combination of our three submodels. To investigate the efficiency of the division into the three subgroups, another model is trained based on all 105, 627 shots taking all features from Table 2 including the information whether the shot was a header, a leg-shot from open play or a direct freekick.

Table 2 . Statistical evaluation of the expected goal model outcome.

Various standard supervised machine learning models were trained on the training data set, hyperparameters were optimized on the validation data set and the models' accuracy's were evaluated on the test data set. Naturally, the necessary hyperparameters depend on the machine learning algorithm. In the case of the extreme gradient boosting model (hereafter referred to as XGBoost), the parameters we optimized are as follows: Learning rate : controls the step size used per update; Max depth : limits the depth of the tree; Subsample : controls number samples applied to the tree; Min child weight : controls instance weight of a node. For the optimization, we applied Bayesian tree-structured Parzen Estimator hyperparameter optimization approaches for the gradient boosting model ( Bergstra et al., 2011 ; Dewnacker et al., 2016 ; Wang, 2019 ).

For several models in Table 2 , we calculated SHAP values per feature ( Roth and Thomson, 1988 ; Lundberg and Lee, 2017 ; Rodríguez-Pérez and Bajorath, 2020 ). In several applications, using SHAP values 20 instead of standard gain values has proven to be beneficial ( Antipov and Pokryshevskaya, 2020 ; Ibrahim et al., 2020 ; Meng et al., 2020 ).

In order to get a better understanding of the resulting model's accuracy, we implemented two simple models as a baseline models. The first one uses an attribute that is collected for every shot ( chance quality ). This manually collected attribute can contain one of the following two values: sitter or chance. The very simple model now assigns each shot the average conversion rate of the corresponding class. So all shots labeled as chances are assigned a value of 0.063, while the remaining shots labeled as sitters receive a value of 0.548. The second baseline model uses all the event data based features from Table 1 (namely Shot location, Type of shot, Taker ball-control, After freekick , and Freekick ), and train a XGBoost model using these features.

4.3. Statistical Evaluation of the Shot Prediction Model

The first two validation metrics (precision and recall) presented in Table 2 evaluate the outcome of a classification problem. A goal classified with an xG above 50% is classified as a true positive, whereas an unsuccessful shot with an xG below that threshold is defined as a true negative. Thereafter, a recall of 1 could simply be achieved by assigning each shot an xG value above 50%. To incorporate both the true positive and the false positive rate depending on the threshold into our evaluation, we also use the area under the receiving operator curve ( AUC ) as an error function ( Daskivich et al., 2018 ). However, it is our objective to assess the accuracy of the underlying goal scoring probabilities and not just of a binary classification (goal or no goal). While this is possible with the AUC, using the ranked probability score ( RPS ), as presented in Murphy (1970) , fulfills this purpose better, especially for imbalanced data sets.

By splitting up the shots into two groups (chances and sitters), the chance evaluation model ( Table 2 , row 6) achieves a good balance between precision and recall. While this relatively simple model already achieves a somewhat satisfactory RPS of 0.170, the human-made classifications are possibly biased by the shot outcomes. This label is therefore not used as a feature for the remaining prediction models. For the event data based model, the extremely low recall can be interpreted as follows: The model predicts xG value below 50% for most of the shots that actually end up as goals. However, the AUC shows that the event-based model yields more granular predictions than the chance evaluation model. In the direct freekick submodel, no xG prediction exceeds 50%, and therefore its precision is undefined.

Shots are non-deterministic, at the time of the shot, meaning that no model can have a 100% accuracy predicting whether any given shot will score. But what we can expect from our model predictions is that they converge over a large sample. To verify this, we looked at the first 54 matches (matchday one through three) of the 2020/2021 season in Bundesliga and 2nd Bundesliga. Out of the 1, 357 shots, 150 found the back of the net and our model predicted an aggregated xG value of 151.6.

Estimating a team's true strength or its future performances is a crucial unsolved problem in football with many potential use cases ( Goes et al., 2019 ). Both shots on target, two well-established metrics in the literature, have been used for this context ( Lamas et al., 2014 ). Figure 4 displays in which scenarios our xG values fulfills this task better than traditional approaches. It looks at how well you can predict a team's future rest of the season goal ratio (defined as the difference between goals scored and goals conceded) after a certain matchday, by only taking into account one aggregated metric before said matchday. On the y -axis, the correlation between the future goal ratio and the respective metrics (see legend) before that matchday ( x -axis) is shown. Consistently, over all considered seasons a team's historic xG values are able to predict future results better than traditional metrics, especially between matchday 10 and 20. Additionally, we found that in 73.3% of all matches (excluding draws), the winner had a higher xG value 21 , while only in 56.2% of these games, the winning team had more shots, than its opponent.

Figure 4 . Correlation between a team's future goal ratio after a certain matchday and an aggregated metric before said matchday (average of all seasons 2013/2014–2019/2020).

Next, we analyze the features' influence on the predicted goal scoring probability. In the following, we discuss the overall feature importance of our gradient-boosting model trained on all shots with the subcategories as features ( Table 2 , row 1). Figure 5 displays the overall influence according the respective SHAP values per feature on the right, which can be interpreted as an aggregated quantification of the feature's influence. The SHAP values show that the most crucial factors are the shot location (Goal Distance, Angle) and the goalkeeper position (Distance Goalkeeper to Goal) . Maximum Individual Pressure Diff , defined as the difference between the actual pressure and the average pressure given the shot location, has the third highest influence on the predicted values. In Figure 5 (left plot), the x -value of each colored dot displays how a feature influences the model, whereas the color scaling describes the value of the respective feature. Both a flat line and a smooth change of colors (from left to right or vice versa) indicates a roughly linear correlation between the feature value and the model outcome. In Figure 6 , this relationship between the feature values ( x -axis) and influence on the model ( y -axis) is shown more granularly. Although the red line shows a regression, the dispersion of the blue dots provide a deeper insight. Both the left plot in Figure 5 (smooth decrease of the colored dots from left to right) and Figure 6 (red line) shows that the goal distance has an almost linear impact on the predicted values. However, if the distance to the goal is very high, influence relies more on other features, as can be seen by the growing dispersion of the blue dots. The importance of the number defenders in the line of the shot (here Defenders ) underpins the relevance of using positional data, including all opposing players' positions. Looking deeper into the SHAP distributions of this feature, Figure 6 shows an almost linear decrease of the average SHAP value over all shots from zero to four defenders in the line of shot. For more defenders in the line of shot, the average SHAP value—describing a proxy for the features influence—remains mostly constant. In Figure 6 , the feature Goalkeeper in the goal underpins our practitioners' intuitive assumption and can be interpreted as follows: If the goalkeeper is not in the line of shot, it increases the xG value significantly.

Figure 5 . Feature importance according to Shapley values displayed as a SHAP summary plot (left) and global feature contributions by the mean SHAP value across all samples (right) .

Figure 6 . SHAP dependence Plot. For each shot, the respective feature value is plotted on the x-axis vs. the corresponding Shapley values on the y-axis (distance is displayed in meter, and speed is shown in meter per hour).

Again, most of this information would not be available in event data, which highlights the benefit of using both event and positional data once more.

4.4. Evaluation by Subject Matter Expertise

In several workshops with match analysts from Bundesliga clubs and the German national team, the features were defined and ranked according to the estimated influence. These estimations were compared with the above calculated feature importance. Additionally, the SHAP value dispersions and interpretations were discussed in detail. Besides a lot of agreement from practitioners, some statistical results—, e.g., the influence of 4–10 players in the line of shot—were discussed intensively among experts. To evaluate the plausibility of our model from a practitioners perspective, a workshop with selected (assistant) coaches of Bundesliga and 2nd Bundesliga clubs was conducted. For the recently concluded season, the coaches were asked to classify their matches into four categories: deserved or undeserved victories, draws, or losses as in Figure 8 . Afterwards, we compared their labels to the ones produced from our xG model. With a category-accordance of more than 85% (in total 102 matches with 293 goals), practitioners characterized our approach as a helpful tool to assess individual shot qualities and the overall performance of a team.

5. Application and Discussion

5.1. applications.

For the following section, we consider the 2019/2020 season of the German Bundesliga, with in total 306 matches, 954 goals, and 5, 450 shots. We describe how the goal scoring probability xG ( S ) model for a given shot S is aggregated over a season to evaluate teams and players further:

Own goals are not a subtype of a shot event, but rather a separate event type with different attributes. Therefore, they are excluded from our xG calculation. Penalties are assigned an xG value of 0.766, which is the average conversion rate in the Bundesliga history. In the case of so-called double-chance, situations in which a first shot is blocked, but is immediately followed up by a rebound shot, we calculate xG values for each shot. But when we aggregate the team level xG values, we do not want to simply add them up, because it could lead to situations where a teams xG value for small time-window could exceed 1. Therefore, given a double-chance S , defined as two shots within 5 s, we compute the overall probability as:

5.1.1. Teams

Figure 7 displays how many goals each team scored and conceded in comparison to the aggregated xG values our model computed. Consequently, for the 2019/2020 season, BVB (sixth place in the left ranking of Figure 7 ) scored roughly 30 more goals than the sum of all the respective shots' xG values would suggest. Figure 8 provides a closer look at BVB efficiency on a match level. Comparing actual goal differences to the xG differences, the upper right quadrant could be interpreted as deserved wins, where BVB created more promising shot opportunities than their opponents. Matches on the lower right could be interpreted as lucky wins, e.g., the return match 22 against Borussia Mönchengladbach (black and white hatched diamond logo in the bottom right of the left figure).

Figure 7 . Bundesliga 2019/2020 season ranking with aggregated xG and the actual number of goals (xG red, actual goals gray).

Figure 8 . Season report of BVB in season 2019/2020 showing efficiency of BVB matches according to the underlying xG values.

Another match, where our model would have predicted a different result is displayed in Figure 9 23 . The graph shows the aggregated xG values per team over the course of a match. Although SC Freiburg displayed an extraordinary shooting efficiency, by scoring three goals out of three difficult situations, Eintracht Frankfurt created several high quality chances but only converted three of them.

Figure 9 . xG match report of a Bundesliga match between SC Freiburg and Eintracht Frankfurt in season 2019/2020.

Furthermore, our model can help match analysts examine a teams' shooting behavior. Figure 10 presents the number of shots taken vs. the average xG-value per team (left) and for the most scoring strikers (right). Although Fortuna Düsseldorf (red/white logo furthest left in Figure 10 ) had an average xG value (∅(xG)) of 0.08 in the 2019/2020 season, Borussia Mnchengladbach seems to take their shots only in cases of a clear scoring opportunity (∅(xG) = 0.14). FC Bayern Munich (red/blue/white logo top right in Figure 10 ), takes by far the most shots per game. However, with around four less shots per match, Borussia Mönchengladbach has a higher quality of attempts according to our xG model. Comparing FC Augsburg (red/white/green logo with FCA inscription) to Werder Bremen (green diamond logo with a white W as an inscription) shows two distinct patterns. While both teams had a similar number of aggregated xGs over the whole season (see Figure 7 ), Bremen tends to take more shots in less promising situations, while FC Augsburg emphasizes more on taking their shots in situations with a higher goal scoring probability. Having this information for the next opponent prior to each match can help teams to adapt their defending strategy depending on the opponent's shooting preferences.

Figure 10 . Quality vs. quantity of shots taken per team (left) and player (right). The total number of goals scored over the whole season per team and player is displayed in black.

5.1.2. Players

Additionally, we can use player aggregated xG values, both for individual player performance analysis as well as scouting. Comparing Jadon Sancho to Serge Gnabry shows that both players—playing in similar positions and both with very successful teams—have strongly differing shooting patterns. Although Serge Gnabry (top left in Figure 10 ) takes the second most shots per match, Jadon Sancho (lowest in Figure 10 ) takes the fewest shots out of the top 10 scorers, but often in more promising situations according to the xG-values. Besides an overview of strikers shooting behavior in Figure 10 , xG provides a lot more applications to quantify a player's offensive contribution more granularly than traditional metrics.

Since our xG model can be seen as an average across all Bundesliga players' shot efficiency, it can also be used to find players that convert shots at an above average rate. Using this approach, we see that Robert Lewandowski (upper right in Figure 10 ) outscored his aggregated xG value (29.6) by about four goals, scoring a total of 34 in the season out of his 140 shots ( Table 3 , row 12). While this is already an impressive feat, there were in total 11 players, outscoring their xG totals by a larger margin. Jadon Sancho (17 goals/53 shots/8.49 xG agg ) and Erling Haaland (13 goals/34 shots/7.59 xG agg ) lead this category and showed an extraordinary scoring efficiency.

Table 3 . Players with the highest scoring efficiency in the German Bundesliga 2019/2020 season.

5.2. Discussion

We present an xG model that performs better than any of the approaches discussed in the introduction. Rathke (2017) split the pitch into eight zones and trained a logistic regression on each, indirectly taking shot location and angle into consideration. However, their analysis was neither tested on unseen data nor took the positions of defenders and goalkeepers into consideration. By contrast, Lucey et al. (2014) did not only make use of positional data, but also displayed the improvements of the model accuracy. They split all shots into six different game-context situations (open play, counterattack, corner, penalties, freekicks, set pieces) and also learned a regressor for each. Their average error across all shots and scenes is 0.1439. In our final combined model ( Table 2 , row 5), this average error is 0.0928. As a combination of the larger data set (more than 100, 000 shots), our novel synchronization approach (see section 3) and the expert crafted features (see section 4.1) are possible reasons for this improvement.

However, xG models in football are not without flaws. An often criticized point is that they are not evaluating dangerous situations where no shot took place. While this criticism certainly has merits, most offensive actions end up in shots. The official Bundesliga event data include an event type chance without a resulting shot , describing situations, where a team was in a scoring position, but failed to attempt a shot. In our data set, this event occurs on average only 0.93 times per match, underlining that the impact non-shot opportunities have for measuring team performance is rather small. Additionally, as seen in section 5.1, evaluating team strength is not the only application of xG. Shot conversion on team/player level, average shot quality or even on a goalkeeper analysis are insightful use cases that only depend on actual shots taken. Nevertheless, several studies aim to tackle this problem, of noteworthy goal-scoring opportunities without shots, by computing so-called expected possession values ( Link et al., 2016 ; Spearman, 2018 ; Fernández et al., 2019 ), but even these concepts are often build upon a well-calibrated xG model.

Following the logic of expected possession values, it is definitely a potential next step to break the contribution to a goal scored further down to the participating players and their actions. For instance, in the situation described in Figure 2 by assuming shots at several time-points, a simple rule-based approach using our xG model can quantify how much xG Volland added through his dribbling. Another popular extension of xG are expected assists (xA), which measure the likelihood that a pass leading to a shot becomes an assist, by assigning it the resulting xG value. This allows to quantify a player's shot assisting qualities independent of the final shooter's ability to score.

Both the synchronization and the inputs for the xG model heavily rely on the quality of the underlying data. Even for purely event data based xG models, Robberechts (2019) showed that their usefulness strongly depends on the event data quality. One of the parameters causing the biggest inaccuracy in the current model is the ball height. Small objects—like a ball—are hard to track based on video footage, especially due to confusion with replacement balls or other small white objects occurring in the stadium. For header shots, little differences in the ball height have a large impact on the ability of a player to control the placement of a shot causing inaccuracies for our current header model ( Table 2 , row 7). With a steady increase of video camera resolutions and object detection algorithms, we expect a significant improvement for ball tracking. This increase in data quality would likely improve shot synchronization results even further (see section 3.2) and consequently result in even more accurate xG models. Nevertheless, both for tracking data (including ball tracking) and for event data additional evaluation studies to ensure a high data quality for similar projects is essential. Although latest positional and event data provide accurate and detailed information about players, their body orientation and limb tracking could further improve the model's accuracy. For the header model in particular, heights and jumping altitude capacities could be taken into consideration as well.

The harmonization of tracking and event data is not a problem unique to football, which has been barely explored in the literature. In basketball, for instance, the two data sources 24 are mainly used independently of one another ( Tian et al., 2020 ), but as Manisera et al. (2019) noted the combination of both data sources is a crucial future issue. While our algorithm is optimized for football events, it could be adapted and applied to several other sports where both data sources are available.

An accurate expected goals model provides tremendous decision-making support for clubs: Creating many high-quality shooting situations is a crucial indicator of a good performance. To which extent these situations actually end up in goals often depend on random factors or luck. Consequently, a single final match result may not represent the actual team performance accurately. By quantifying a team's conversion rate (goals vs. xG) separately from their aggregated offensive contribution (created xG), clubs can evaluate the performance of their players, teams, and coaches objectively. Future research could even go one step further and explore how this work could affect the way the game is played. One could use our goal probabilities to determine numerically in which situations it is beneficial to shoot, and when one is better of risking an additional dribble or pass. Another area where the use of xG could be explored further are media applications: Recently, media and broadcasting have included xG values in their match coverage. For each goal occurring in German Bundesliga, different broadcasters have chosen to display our xG value seconds after the goal occurred 25 .

Now that the amount of data-driven approaches to support tactical analysis in football is increasing ( Goes et al., 2020 ), more qualitative studies might help to underpin the statistical evaluation of models like xG. Although we present a first attempt toward an expert-based evaluation of our approaches (see sections 3.2 and 4.4), there is a lot of potential for further investigations, which could also serve to establish data-driven methods in the sport science and football community.

6. Conclusion

We present a meaningful proxy for goals scored in football, which helps to evaluate players' and teams' performance more accurately and objectively. Our xG model is based on a huge data set of cutting-edge and consistently acquired positional and event data that we combined using our own synchronization algorithm.

It exceeds traditional metrics significantly when evaluating strikers' ( Table 3 ) and teams' ( Figure 7 ) scoring efficiency, when evaluating single match performances (i.e., teams with higher xG win 73.3% of all not-drawn matches) and even when predicting future match results ( Figure 3 ). It also allows us to evaluate assist performances of players independent of the striker's final touch. Additionally, several future potentials are shown for sport and data science research.

Data Availability Statement

The data analyzed in this study is subject to the following licenses/restrictions: data are property of DFL/DFB e.V. and thus can not be shared publicly. Requests to access these datasets should be directed to Sportec Solutions AG, DFL e.V., DFB e.V.

Ethics Statement

Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.

Author Contributions

GA was responsible for the implementation of the approach and involved in all discussions with practitioners. PB focused on the practical evaluation and the communication with practitioners. Both authors conducted the scientific studies together and were equally involved in the writing process of the manuscript.

Conflict of Interest

GA was employed by the company Sportec Solutions AG and PB was employed by the company DFB-Akademie (Deutscher Fußball-Bund e.V).

Acknowledgments

This work would not have been possible without the perspective of professional match analysts from world class teams who helped us to define relevant features and spend much time evaluating (intermediate) results. We would cordially like to thank Dr. Stephan Nopp and Christofer Clemens (match analysts of the German National team), Jannis Scheibe (head match-analyst of the German U21 mens national team) as well as Sebastian Geißler (former match-analyst of Borussia Mönchengladbach). Additionally, the authors would like to thank Dr. Hendrik Weber and Deutsche Fußball Liga (DFL)/Sportec Solutions AG for providing the positional and event data.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fspor.2021.624475/full#supplementary-material

We also provide an example of the media live application in German Bundesliga 26 .

1. ^ https://www.goal.com/en-us/lists/cristiano-ronaldo-lionel-messi-pele-who-are-the-top-goal-scorers-/ynctx2o9fa371vi1x0dsgr0np (accessed July 10, 2020).

2. ^ https://www.givemesport.com/1534019-the-top-10-players-with-the-most-assists-in-europes-top-five-leagues-this-decade (accessed July 8, 2020).

3. ^ https://fivethirtyeight.com/features/how-mapping-shots-in-the-nba-changed-it-forever/ (accessed July 10, 2020).

4. ^ https://www.americansocceranalysis.com/home/2017/3/6/validating-the-asa-xgoals-model (accessed October 24, 2020).

5. ^ http://www.northyardanalytics.com/blog/2015/08/22/pitfalls-of-measuring-shooting-and-saving-skill/ (accessed October 24, 2020).

6. ^ https://www.optasports.com/services/analytics/advanced-metrics/ (accessed October 24, 2020).

7. ^ https://differentgame.wordpress.com/2014/05/19/a-shooting-model-an-expglanation-and-application/ (accessed October 24, 2020).

8. ^ The situation in the right plot is also displayed in Figure 2 . The respective video can be found here: https://www.youtube.com/watch?v=UdvrKfsJISY&feature=onebox&t=1m08s (accessed October 24, 2020).

9. ^ https://s.bundesliga.com/assets/doc/10000/2189_original.pdf (accessed September 10, 2020).

10. ^ https://chyronhego.com/wp-content/uploads/2019/01/TRACAB-PI-sheet.pdf (accessed September 10, 2020).

11. ^ Note that the Gen 5 system has been in use since season 2019/2020, while all prior Bundesliga seasons were tracked using the Gen 4 TRACAB version.

12. ^ The ground truth was measured by a VICON system, using an optoelectronic motion capture system based on markers placed on the tracked objects. Further details about this system can be found here: https://www.vicon.com/ . An evaluation study of that system can be found in Merriaux et al. (2017) .

13. ^ The study was conducted by the Fédération Internationale de Football Association (FIFA) in close cooperation with the Victoria University (Melbourne, Australia). An overview of the study can be found here: https://football-technology.fifa.com/en/media-tiles/fifa-quality-performance-reports-for-epts/ , the report of the Gen 5 system can be found here: https://football-technology.fifa.com/media/172171/chyronhegoopt-fifa-epts-report-nov2018.pdf (accessed December 26, 2020).

14. ^ https://www.youtube.com/watch?v=UdvrKfsJISY&feature=onebox&t=1m08s (accessed September 10, 2020).

15. ^ Xml stands for e X tensible M arkup L anguage and is an established format to transfer complex data files.

16. ^ https://www.hudl.com/products/sportscode (accessed June 20, 2020).

17. ^ We use a range here, because both, harmonizing the different video and data sources and the manual selection of the shot timestamp, may cause slight time discrepancies.

18. ^ In the first sequence, actual match-footage of a scene is shown. The second shows a 2D animation of the same scene, with a frame-counter on top. This frame counter counts down till 0 where the shot happened and increases afterwards again. The third sequence combines both video sources together (see Supplementary Video 1 ).

19. ^ See Supplementary Video 2 .

20. ^ The abbreviation SHAP stands for SH apley A dditive ex P lanation.

21. ^ On a match and team level the overall xG balance between the two teams is considered here. For both teams, we sum up the xG values per team of all their shots.

22. ^ https://www.youtube.com/watch?v=RUaORAinaoc&feature=onebox (accessed October 2, 2020).

23. ^ https://www.youtube.com/watch?v=jl1C0KsIqaQ (accessed October 2, 2020).

24. ^ In basketball, event level data are often referred to as play-by-play data.

25. ^ https://www.dfl.de/en/news/bundesliga-and-amazon-web-services-to-develop-next-generation-football-viewing-experience/ (accessed September 10, 2020).

26. ^ https://www.youtube.com/watch?v=5flVB9ef0uM .

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Keywords: expected goals, XG, positional data, event data, applied machine learning, football, soccer, sports analytics

Citation: Anzer G and Bauer P (2021) A Goal Scoring Probability Model for Shots Based on Synchronized Positional and Event Data in Football (Soccer). Front. Sports Act. Living 3:624475. doi: 10.3389/fspor.2021.624475

Received: 31 October 2020; Accepted: 15 February 2021; Published: 29 March 2021.

Reviewed by:

Copyright © 2021 Anzer and Bauer. 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: Gabriel Anzer, gabrielanzer@gmail.com

† ORCID: Gabriel Anzer orcid.org/0000-0003-3129-8359 Pascal Bauer orcid.org/0000-0001-8613-6635

This article is part of the Research Topic

Using Artificial Intelligence to Enhance Sport Performance

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Which social media platforms are most common, who uses each social media platform, find out more, social media fact sheet.

Many Americans use social media to connect with one another, engage with news content, share information and entertain themselves. Explore the patterns and trends shaping the social media landscape.

To better understand Americans’ social media use, Pew Research Center surveyed 5,733 U.S. adults from May 19 to Sept. 5, 2023. Ipsos conducted this National Public Opinion Reference Survey (NPORS) for the Center using address-based sampling and a multimode protocol that included both web and mail. This way nearly all U.S. adults have a chance of selection. The survey is weighted to be representative of the U.S. adult population by gender, race and ethnicity, education and other categories.

Polls from 2000 to 2021 were conducted via phone. For more on this mode shift, read our Q&A.

Here are the questions used for this analysis , along with responses, and  its methodology ­­­.

A note on terminology: Our May-September 2023 survey was already in the field when Twitter changed its name to “X.” The terms  Twitter  and  X  are both used in this report to refer to the same platform.

YouTube and Facebook are the most-widely used online platforms. About half of U.S. adults say they use Instagram, and smaller shares use sites or apps such as TikTok, LinkedIn, Twitter (X) and BeReal.

Note: The vertical line indicates a change in mode. Polls from 2012-2021 were conducted via phone. In 2023, the poll was conducted via web and mail. For more details on this shift, please read our Q&A . Refer to the topline for more information on how question wording varied over the years. Pre-2018 data is not available for YouTube, Snapchat or WhatsApp; pre-2019 data is not available for Reddit; pre-2021 data is not available for TikTok; pre-2023 data is not available for BeReal. Respondents who did not give an answer are not shown.

Source: Surveys of U.S. adults conducted 2012-2023.

Usage of the major online platforms varies by factors such as age, gender and level of formal education.

% of U.S. adults who say they ever use __ by …

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This fact sheet was compiled by Research Assistant  Olivia Sidoti , with help from Research Analyst  Risa Gelles-Watnick , Research Analyst  Michelle Faverio , Digital Producer  Sara Atske , Associate Information Graphics Designer Kaitlyn Radde and Temporary Researcher  Eugenie Park .

Follow these links for more in-depth analysis of the impact of social media on American life.

• Americans’ Social Media Use  Jan. 31, 2024
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• Q&A: How and why we’re changing the way we study tech adoption  Jan. 31, 2024

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Bloom’s taxonomy.

Background Information | The Original Taxonomy | The Revised Taxonomy | Why Use Bloom’s Taxonomy? | Further Information

The above graphic is released under a Creative Commons Attribution license. You’re free to share, reproduce, or otherwise use it, as long as you attribute it to the Vanderbilt University Center for Teaching. For a higher resolution version, visit our Flickr account and look for the “Download this photo” icon.

Background Information

In 1956, Benjamin Bloom with collaborators Max Englehart, Edward Furst, Walter Hill, and David Krathwohl published a framework for categorizing educational goals: Taxonomy of Educational Objectives . Familiarly known as Bloom’s Taxonomy , this framework has been applied by generations of K-12 teachers and college instructors in their teaching.

The framework elaborated by Bloom and his collaborators consisted of six major categories: Knowledge, Comprehension, Application, Analysis, Synthesis, and Evaluation. The categories after Knowledge were presented as “skills and abilities,” with the understanding that knowledge was the necessary precondition for putting these skills and abilities into practice.

While each category contained subcategories, all lying along a continuum from simple to complex and concrete to abstract, the taxonomy is popularly remembered according to the six main categories.

The Original Taxonomy (1956)

Here are the authors’ brief explanations of these main categories in from the appendix of Taxonomy of Educational Objectives ( Handbook One , pp. 201-207):

• Knowledge “involves the recall of specifics and universals, the recall of methods and processes, or the recall of a pattern, structure, or setting.”
• Comprehension “refers to a type of understanding or apprehension such that the individual knows what is being communicated and can make use of the material or idea being communicated without necessarily relating it to other material or seeing its fullest implications.”
• Application refers to the “use of abstractions in particular and concrete situations.”
• Analysis represents the “breakdown of a communication into its constituent elements or parts such that the relative hierarchy of ideas is made clear and/or the relations between ideas expressed are made explicit.”
• Synthesis involves the “putting together of elements and parts so as to form a whole.”
• Evaluation engenders “judgments about the value of material and methods for given purposes.”

The 1984 edition of Handbook One is available in the CFT Library in Calhoun 116. See its ACORN record for call number and availability.

Barbara Gross Davis, in the “Asking Questions” chapter of Tools for Teaching , also provides examples of questions corresponding to the six categories. This chapter is not available in the online version of the book, but Tools for Teaching is available in the CFT Library. See its ACORN record for call number and availability.

The Revised Taxonomy (2001)

A group of cognitive psychologists, curriculum theorists and instructional researchers, and testing and assessment specialists published in 2001 a revision of Bloom’s Taxonomy with the title A Taxonomy for Teaching, Learning, and Assessment . This title draws attention away from the somewhat static notion of “educational objectives” (in Bloom’s original title) and points to a more dynamic conception of classification.

The authors of the revised taxonomy underscore this dynamism, using verbs and gerunds to label their categories and subcategories (rather than the nouns of the original taxonomy). These “action words” describe the cognitive processes by which thinkers encounter and work with knowledge:

• Recognizing
• Interpreting
• Exemplifying
• Classifying
• Summarizing
• Implementing
• Differentiating
• Attributing

In the revised taxonomy, knowledge is at the basis of these six cognitive processes, but its authors created a separate taxonomy of the types of knowledge used in cognition:

• Knowledge of terminology
• Knowledge of specific details and elements
• Knowledge of classifications and categories
• Knowledge of principles and generalizations
• Knowledge of theories, models, and structures
• Knowledge of subject-specific skills and algorithms
• Knowledge of subject-specific techniques and methods
• Knowledge of criteria for determining when to use appropriate procedures
• Strategic Knowledge
• Knowledge about cognitive tasks, including appropriate contextual and conditional knowledge
• Self-knowledge

Mary Forehand from the University of Georgia provides a guide to the revised version giving a brief summary of the revised taxonomy and a helpful table of the six cognitive processes and four types of knowledge.

Why Use Bloom’s Taxonomy?

The authors of the revised taxonomy suggest a multi-layered answer to this question, to which the author of this teaching guide has added some clarifying points:

• Objectives (learning goals) are important to establish in a pedagogical interchange so that teachers and students alike understand the purpose of that interchange.
• Organizing objectives helps to clarify objectives for themselves and for students.
• “plan and deliver appropriate instruction”;
• “design valid assessment tasks and strategies”;and
• “ensure that instruction and assessment are aligned with the objectives.”

Citations are from A Taxonomy for Learning, Teaching, and Assessing: A Revision of Bloom’s Taxonomy of Educational Objectives .

Further Information

Section III of A Taxonomy for Learning, Teaching, and Assessing: A Revision of Bloom’s Taxonomy of Educational Objectives , entitled “The Taxonomy in Use,” provides over 150 pages of examples of applications of the taxonomy. Although these examples are from the K-12 setting, they are easily adaptable to the university setting.

Section IV, “The Taxonomy in Perspective,” provides information about 19 alternative frameworks to Bloom’s Taxonomy, and discusses the relationship of these alternative frameworks to the revised Bloom’s Taxonomy.

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Net Zero by 2050

A Roadmap for the Global Energy Sector

This report is part of Net Zero Emissions

About this report

The number of countries announcing pledges to achieve net zero emissions over the coming decades continues to grow. But the pledges by governments to date – even if fully achieved – fall well short of what is required to bring global energy-related carbon dioxide emissions to net zero by 2050 and give the world an even chance of limiting the global temperature rise to 1.5 °C. This special report is the world’s first comprehensive study of how to transition to a net zero energy system by 2050 while ensuring stable and affordable energy supplies, providing universal energy access, and enabling robust economic growth. It sets out a cost-effective and economically productive pathway, resulting in a clean, dynamic and resilient energy economy dominated by renewables like solar and wind instead of fossil fuels. The report also examines key uncertainties, such as the roles of bioenergy, carbon capture and behavioural changes in reaching net zero.

Summary for policy makers

Reaching net zero emissions globally by 2050 is a critical and formidable goal.

The energy sector is the source of around three-quarters of greenhouse gas emissions today and holds the key to averting the worst effects of climate change, perhaps the greatest challenge humankind has faced. Reducing global carbon dioxide (CO 2 ) emissions to net zero by 2050 is consistent with efforts to limit the long-term increase in average global temperatures to 1.5˚C. This calls for nothing less than a complete transformation of how we produce, transport and consume energy. The growing political consensus on reaching net zero is cause for considerable optimism about the progress the world can make, but the changes required to reach net zero emissions globally by 2050 are poorly understood. A huge amount of work is needed to turn today’s impressive ambitions into reality, especially given the range of different situations among countries and their differing capacities to make the necessary changes. This special IEA report sets out a pathway for achieving this goal, resulting in a clean and resilient energy system that would bring major benefits for human prosperity and well-being.

The global pathway to net zero emissions by 2050 detailed in this report requires all governments to significantly strengthen and then successfully implement their energy and climate policies. Commitments made to date fall far short of what is required by that pathway. The number of countries that have pledged to achieve net zero emissions has grown rapidly over the last year and now covers around 70% of global emissions of CO 2 . This is a huge step forward. However, most pledges are not yet underpinned by near-term policies and measures. Moreover, even if successfully fulfilled, the pledges to date would still leave around 22 billion tonnes of CO 2 emissions worldwide in 2050. The continuation of that trend would be consistent with a temperature rise in 2100 of around 2.1 °C. Global emissions fell in 2020 because of the Covid-19 crisis but are already rebounding strongly as economies recover. Further delay in acting to reverse that trend will put net zero by 2050 out of reach.

In this Summary for Policy Makers, we outline the essential conditions for the global energy sector to reach net zero CO 2 emissions by 2050. The pathway described in depth in this report achieves this objective with no offsets from outside the energy sector, and with low reliance on negative emissions technologies. It is designed to maximise technical feasibility, cost-effectiveness and social acceptance while ensuring continued economic growth and secure energy supplies. We highlight the priority actions that are needed today to ensure the opportunity of net zero by 2050 – narrow but still achievable – is not lost. The report provides a global view, but countries do not start in the same place or finish at the same time: advanced economies have to reach net zero before emerging markets and developing economies, and assist others in getting there. We also recognise that the route mapped out here is a path, not necessarily the path, and so we examine some key uncertainties, notably concerning the roles played by bioenergy, carbon capture and behavioural changes. Getting to net zero will involve countless decisions by people across the world, but our primary aim is to inform the decisions made by policy makers, who have the greatest scope to move the world closer to its climate goals.

Net zero by 2050 hinges on an unprecedented clean technology push to 2030

The path to net zero emissions is narrow: staying on it requires immediate and massive deployment of all available clean and efficient energy technologies. In the net zero emissions pathway presented in this report, the world economy in 2030 is some 40% larger than today but uses 7% less energy. A major worldwide push to increase energy efficiency is an essential part of these efforts, resulting in the annual rate of energy intensity improvements averaging 4% to 2030 – about three-times the average rate achieved over the last two decades. Emissions reductions from the energy sector are not limited to CO 2 : in our pathway, methane emissions from fossil fuel supply fall by 75% over the next ten years as a result of a global, concerted effort to deploy all available abatement measures and technologies.

Ever-cheaper renewable energy technologies give electricity the edge in the race to zero. Our pathway calls for scaling up solar and wind rapidly this decade, reaching annual additions of 630 gigawatts (GW) of solar photovoltaics (PV) and 390 GW of wind by 2030, four-times the record levels set in 2020. For solar PV, this is equivalent to installing the world’s current largest solar park roughly every day. Hydropower and nuclear, the two largest sources of low-carbon electricity today, provide an essential foundation for transitions. As the electricity sector becomes cleaner, electrification emerges as a crucial economy-wide tool for reducing emissions. Electric vehicles (EVs) go from around 5% of global car sales to more than 60% by 2030.  

Priority action: Make the 2020s the decade of massive clean energy expansion

All the technologies needed to achieve the necessary deep cuts in global emissions by 2030 already exist, and the policies that can drive their deployment are already proven.

As the world continues to grapple with the impacts of the Covid-19 pandemic, it is essential that the resulting wave of investment and spending to support economic recovery is aligned with the net zero pathway. Policies should be strengthened to speed the deployment of clean and efficient energy technologies. Mandates and standards are vital to drive consumer spending and industry investment into the most efficient technologies. Targets and competitive auctions can enable wind and solar to accelerate the electricity sector transition. Fossil fuel subsidy phase-outs, carbon pricing and other market reforms can ensure appropriate price signals. Policies should limit or provide disincentives for the use of certain fuels and technologies, such as unabated coal-fired power stations, gas boilers and conventional internal combustion engine vehicles. Governments must lead the planning and incentivising of the massive infrastructure investment, including in smart transmission and distribution grids.

Electric car sales in the net zero pathway, 2020-2030

Capacity additions of solar pv and wind in the net zero pathway, 2020-2030, energy intensity of gdp in the net zero pathway, 2020-2030, net zero by 2050 requires huge leaps in clean energy innovation.

Reaching net zero by 2050 requires further rapid deployment of available technologies as well as widespread use of technologies that are not on the market yet. Major innovation efforts must occur over this decade in order to bring these new technologies to market in time. Most of the global reductions in CO 2 emissions through 2030 in our pathway come from technologies readily available today. But in 2050, almost half the reductions come from technologies that are currently at the demonstration or prototype phase. In heavy industry and long-distance transport, the share of emissions reductions from technologies that are still under development today is even higher.

The biggest innovation opportunities concern advanced batteries, hydrogen electrolysers, and direct air capture and storage. Together, these three technology areas make vital contributions the reductions in CO 2 emissions between 2030 and 2050 in our pathway. Innovation over the next ten years – not only through research and development (R&D) and demonstration but also through deployment – needs to be accompanied by the large-scale construction of the infrastructure the technologies will need. This includes new pipelines to transport captured CO 2 emissions and systems to move hydrogen around and between ports and industrial zones.

Priority action: Prepare for the next phase of the transition by boosting innovation

Clean energy innovation must accelerate rapidly, with governments putting R&D, demonstration and deployment at the core of energy and climate policy.

Government R&D spending needs to be increased and reprioritised. Critical areas such as electrification, hydrogen, bioenergy and carbon capture, utilisation and storage (CCUS) today receive only around one-third of the level of public R&D funding of the more established low-carbon electricity generation and energy efficiency technologies. Support is also needed to accelerate the roll-out of demonstration projects, to leverage private investment in R&D, and to boost overall deployment levels to help reduce costs. Around USD 90 billion of public money needs to be mobilised globally as soon as possible to complete a portfolio of demonstration projects before 2030. Currently, only roughly USD 25 billion is budgeted for that period. Developing and deploying these technologies would create major new industries, as well as commercial and employment opportunities.

Annual CO2 emissions savings in the net zero pathway, 2030 and 2050, relative to 2020

The transition to net zero is for and about people.

A transition of the scale and speed described by the net zero pathway cannot be achieved without sustained support and participation from citizens. The changes will affect multiple aspects of people’s lives – from transport, heating and cooking to urban planning and jobs. We estimate that around 55% of the cumulative emissions reductions in the pathway are linked to consumer choices such as purchasing an EV, retrofitting a house with energy-efficient technologies or installing a heat pump. Behavioural changes, particularly in advanced economies – such as replacing car trips with walking, cycling or public transport, or foregoing a long-haul flight – also provide around 4% of the cumulative emissions reductions.

Providing electricity to around 785 million people that have no access and clean cooking solutions to 2.6 billion people that lack those options is an integral part of our pathway. Emissions reductions have to go hand-in-hand with efforts to ensure energy access for all by 2030. This costs around USD 40 billion a year, equal to around 1% of average annual energy sector investment, while also bringing major co-benefits from reduced indoor air pollution.

Some of the changes brought by the clean energy transformation may be challenging to implement, so decisions must be transparent, just and cost-effective. Governments need to ensure that clean energy transitions are people-centred and inclusive. Household energy expenditure as a share of disposable income – including purchases of efficient appliances and fuel bills – rises modestly in emerging market and developing economies in our net zero pathway as more people gain access to energy and demand for modern energy services increases rapidly. Ensuring the affordability of energy for households demands close attention: policy tools that can direct support to the poorest include tax credits, loans and targeted subsidies.

Priority action: Clean energy jobs will grow strongly but must be spread widely

Energy transitions have to take account of the social and economic impacts on individuals and communities, and treat people as active participants.

The transition to net zero brings substantial new opportunities for employment, with 14 million jobs created by 2030 in our pathway thanks to new activities and investment in clean energy. Spending on more efficient appliances, electric and fuel cell vehicles, and building retrofits and energy-efficient construction would require a further 16 million workers. But these opportunities are often in different locations, skill sets and sectors than the jobs that will be lost as fossil fuels decline. In our pathway, around 5 million jobs are lost. Most of those jobs are located close to fossil fuel resources, and many are well paid, meaning structural changes can cause shocks for communities with impacts that persist over time. This requires careful policy attention to address the employment losses. It will be vital to minimise hardships associated with these disruptions, such as by retraining workers, locating new clean energy facilities in heavily affected areas wherever possible, and providing regional aid.

Global employment in energy supply in the Net Zero Scenario, 2019-2030

An energy sector dominated by renewables.

In the net zero pathway, global energy demand in 2050 is around 8% smaller than today, but it serves an economy more than twice as big and a population with 2 billion more people. More efficient use of energy, resource efficiency and behavioural changes combine to offset increases in demand for energy services as the world economy grows and access to energy is extended to all.

Instead of fossil fuels, the energy sector is based largely on renewable energy. Two-thirds of total energy supply in 2050 is from wind, solar, bioenergy, geothermal and hydro energy. Solar becomes the largest source, accounting for one-fifth of energy supplies. Solar PV capacity increases 20-fold between now and 2050, and wind power 11-fold.

Net zero means a huge decline in the use of fossil fuels. They fall from almost four-fifths of total energy supply today to slightly over one-fifth by 2050. Fossil fuels that remain in 2050 are used in goods where the carbon is embodied in the product such as plastics, in facilities fitted with CCUS, and in sectors where low-emissions technology options are scarce.

Electricity accounts for almost 50% of total energy consumption in 2050. It plays a key role across all sectors – from transport and buildings to industry – and is essential to produce low-emissions fuels such as hydrogen. To achieve this, total electricity generation increases over two-and-a-half-times between today and 2050. At the same time, no additional new final investment decisions should be taken for new unabated coal plants, the least efficient coal plants are phased out by 2030, and the remaining coal plants still in use by 2040 are retrofitted. By 2050, almost 90% of electricity generation comes from renewable sources, with wind and solar PV together accounting for nearly 70%. Most of the remainder comes from nuclear.    

Emissions from industry, transport and buildings take longer to reduce. Cutting industry emissions by 95% by 2050 involves major efforts to build new infrastructure. After rapid innovation progress through R&D, demonstration and initial deployment between now and 2030 to bring new clean technologies to market, the world then has to put them into action. Every month from 2030 onwards, ten heavy industrial plants are equipped with CCUS, three new hydrogen-based industrial plants are built, and 2 GW of electrolyser capacity are added at industrial sites. Policies that end sales of new internal combustion engine cars by 2035 and boost electrification underpin the massive reduction in transport emissions. In 2050, cars on the road worldwide run on electricity or fuel cells. Low-emissions fuels are essential where energy needs cannot easily or economically be met by electricity. For example, aviation relies largely on biofuels and synthetic fuels, and ammonia is vital for shipping. In buildings, bans on new fossil fuel boilers need to start being introduced globally in 2025, driving up sales of electric heat pumps. Most old buildings and all new ones comply with zero-carbon-ready building energy codes. 1

Priority action: Set near-term milestones to get on track for long-term targets

Governments need to provide credible step-by-step plans to reach their net zero goals, building confidence among investors, industry, citizens and other countries.

Governments must put in place long-term policy frameworks to allow all branches of government and stakeholders to plan for change and facilitate an orderly transition. Long-term national low-emissions strategies, called for by the Paris Agreement, can set out a vision for national transitions, as this report has done on a global level. These long-term objectives need to be linked to measurable short-term targets and policies. Our pathway details more than 400 sectoral and technology milestones to guide the global journey to net zero by 2050.  

There is no need for investment in new fossil fuel supply in our net zero pathway

Beyond projects already committed as of 2021, there are no new oil and gas fields approved for development in our pathway, and no new coal mines or mine extensions are required. The unwavering policy focus on climate change in the net zero pathway results in a sharp decline in fossil fuel demand, meaning that the focus for oil and gas producers switches entirely to output – and emissions reductions – from the operation of existing assets. Unabated coal demand declines by 98% to just less than 1% of total energy use in 2050. Gas demand declines by 55% to 1 750 billion cubic metres and oil declines by 75% to 24 million barrels per day (mb/d), from around 90 mb/d in 2020.

Clean electricity generation, network infrastructure and end-use sectors are key areas for increased investment. Enabling infrastructure and technologies are vital for transforming the energy system. Annual investment in transmission and distribution grids expands from USD 260 billion today to USD 820 billion in 2030. The number of public charging points for EVs rises from around 1 million today to 40 million in 2030, requiring annual investment of almost USD 90 billion in 2030. Annual battery production for EVs leaps from 160 gigawatt-hours (GWh) today to 6 600 GWh in 2030 – the equivalent of adding almost 20 gigafactories 2  each year for the next ten years. And the required roll-out of hydrogen and CCUS after 2030 means laying the groundwork now: annual investment in CO 2 pipelines and hydrogen-enabling infrastructure increases from USD 1 billion today to around USD 40 billion in 2030.

Priority action: Drive a historic surge in clean energy investment

Policies need to be designed to send market signals that unlock new business models and mobilise private spending, especially in emerging economies.

Accelerated delivery of international public finance will be critical to energy transitions, especially in developing economies, but ultimately the private sector will need to finance most of the extra investment required. Mobilising the capital for large-scale infrastructure calls for closer co operation between developers, investors, public financial institutions and governments. Reducing risks for investors will be essential to ensure successful and affordable clean energy transitions. Many emerging market and developing economies, which rely mainly on public funding for new energy projects and industrial facilities, will need to reform their policy and regulatory frameworks to attract more private finance. International flows of long-term capital to these economies will be needed to support the development of both existing and emerging clean energy technologies.

Clean energy investment in the net zero pathway, 2016-2050

An unparalleled clean energy investment boom lifts global economic growth.

Total annual energy investment surges to USD 5 trillion by 2030, adding an extra 0.4 percentage point a year to annual global GDP growth, based on our joint analysis with the International Monetary Fund. This unparalleled increase – with investment in clean energy and energy infrastructure more than tripling already by 2030 – brings significant economic benefits as the world emerges from the Covid-19 crisis. The jump in private and government spending creates millions of jobs in clean energy, including energy efficiency, as well as in the engineering, manufacturing and construction industries. All of this puts global GDP 4% higher in 2030 than it would be based on current trends.

Governments have a key role in enabling investment-led growth and ensuring that the benefits are shared by all. There are large differences in macroeconomic impacts between regions. But government investment and public policies are essential to attract large amounts of private capital and to help offset the declines in fossil fuel income that many countries will experience. The major innovation efforts needed to bring new clean energy technologies to market could boost productivity and create entirely new industries, providing opportunities to locate them in areas that see job losses in incumbent industries. Improvements in air quality provide major health benefits, with 2 million fewer premature deaths globally from air pollution in 2030 than today in our net zero pathway. Achieving universal energy access by 2030 would provide a major boost to well-being and productivity in developing economies.

New energy security concerns emerge, and old ones remain

The contraction of oil and natural gas production will have far-reaching implications for all the countries and companies that produce these fuels. No new oil and natural gas fields are needed in our pathway, and oil and natural gas supplies become increasingly concentrated in a small number of low-cost producers. For oil, the OPEC share of a much-reduced global oil supply increases from around 37% in recent years to 52% in 2050, a level higher than at any point in the history of oil markets. Yet annual per capita income from oil and natural gas in producer economies falls by about 75%, from USD 1 800 in recent years to USD 450 by the 2030s, which could have knock-on societal effects. Structural reforms and new sources of revenue are needed, even though these are unlikely to compensate fully for the drop in oil and gas income. While traditional supply activities decline, the expertise of the oil and natural gas industry fits well with technologies such as hydrogen, CCUS and offshore wind that are needed to tackle emissions in sectors where reductions are likely to be most challenging.

The energy transition requires substantial quantities of critical minerals, and their supply emerges as a significant growth area. The total market size of critical minerals like copper, cobalt, manganese and various rare earth metals grows almost sevenfold between 2020 and 2030 in the net zero pathway. Revenues from those minerals are larger than revenues from coal well before 2030. This creates substantial new opportunities for mining companies. It also creates new energy security concerns, including price volatility and additional costs for transitions, if supply cannot keep up with burgeoning demand.

The rapid electrification of all sectors makes electricity even more central to energy security around the world than it is today. Electricity system flexibility – needed to balance wind and solar with evolving demand patterns – quadruples by 2050 even as retirements of fossil fuel capacity reduce conventional sources of flexibility. The transition calls for major increases in all sources of flexibility: batteries, demand response and low-carbon flexible power plants, supported by smarter and more digital electricity networks. The resilience of electricity systems to cyberattacks and other emerging threats needs to be enhanced.

Priority action: Address emerging energy security risks now

Ensuring uninterrupted and reliable supplies of energy and critical energy-related commodities at affordable prices will only rise in importance on the way to net zero.

The focus of energy security will evolve as reliance on renewable electricity grows and the role of oil and gas diminishes. Potential vulnerabilities from the increasing importance of electricity include the variability of supply and cybersecurity risks. Governments need to create markets for investment in batteries, digital solutions and electricity grids that reward flexibility and enable adequate and reliable supplies of electricity. The growing dependence on critical minerals required for key clean energy technologies calls for new international mechanisms to ensure both the timely availability of supplies and sustainable production. At the same time, traditional energy security concerns will not disappear, as oil production will become more concentrated.

Critical minerals demand in the net zero pathway, 2020-2050

Oil supply in the net zero pathway, 2020-2050, international co-operation is pivotal for achieving net zero emissions by 2050.

Making net zero emissions a reality hinges on a singular, unwavering focus from all governments – working together with one another, and with businesses, investors and citizens. All stakeholders need to play their part. The wide-ranging measures adopted by governments at all levels in the net zero pathway help to frame, influence and incentivise the purchase by consumers and investment by businesses. This includes how energy companies invest in new ways of producing and supplying energy services, how businesses invest in equipment, and how consumers cool and heat their homes, power their devices and travel.

Underpinning all these changes are policy decisions made by governments. Devising cost-effective national and regional net zero roadmaps demands co-operation among all parts of government that breaks down silos and integrates energy into every country’s policy making on finance, labour, taxation, transport and industry. Energy or environment ministries alone cannot carry out the policy actions needed to reach net zero by 2050.

Changes in energy consumption result in a significant decline in fossil fuel tax revenues. In many countries today, taxes on diesel, gasoline and other fossil fuel consumption are an important source of public revenues, providing as much as 10% in some cases. In the net zero pathway, tax revenue from oil and gas retail sales falls by about 40% between 2020 and 2030. Managing this decline will require long-term fiscal planning and budget reforms.

The net zero pathway relies on unprecedented international co-operation among governments, especially on innovation and investment. The IEA stands ready to support governments in preparing national and regional net zero roadmaps, to provide guidance and assistance in implementing them, and to promote international co-operation to accelerate the energy transition worldwide. 

Priority action: Take international co-operation to new heights

This is not simply a matter of all governments seeking to bring their national emissions to net zero – it means tackling global challenges through co-ordinated actions.

Governments must work together in an effective and mutually beneficial manner to implement coherent measures that cross borders. This includes carefully managing domestic job creation and local commercial advantages with the collective global need for clean energy technology deployment. Accelerating innovation, developing international standards and co-ordinating to scale up clean technologies needs to be done in a way that links national markets. Co-operation must recognise differences in the stages of development of different countries and the varying situations of different parts of society. For many rich countries, achieving net zero emissions will be more difficult and costly without international co-operation. For many developing countries, the pathway to net zero without international assistance is not clear. Technical and financial support is needed to ensure deployment of key technologies and infrastructure. Without greater international co-operation, global CO 2 emissions will not fall to net zero by 2050. 

Global energy-related CO2 emissions in the net zero pathway and Low International Cooperation Case, 2010-2090

A zero-carbon-ready building is highly energy efficient and either uses renewable energy directly or uses an energy supply that will be fully decarbonised by 2050, such as electricity or district heat.

Battery gigafactory capacity assumption = 35 gigawatt-hours per year.

Reference 1

Reference 2, related net zero reports.

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Week 35 rsk pool papers 2022: soccer, bob morton, capital intl, winstar, bigwin, rsk papers week 35 2022.

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• Week 10 Pool Result for Sat 9 Sep 2023 – UK 2023/2024
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Week 32 Pools RSK Papers 2024: Soccer X Research, Bob Morton, Capital Intl, Winstar, BigWin

February 4, 2024 Obinna FOOTBALL POOLS , POOL RSK PAPERS 4

Week 32 Pool RSK papers page

Here, we furnish you with weekly and current pool RSK papers for your forecast and winning pleasure.  Click on the images to view them more clearly. Enjoy

RSK PAPERS: Soccer X Research, Bob Morton, Capital International

Special advance.

Right On Fixtures

Temple of Draw

Soccer X Researc h

Capital International

Pools Telegraph

Dream International Research…

Available on demand..Whatsapp Only +2347068570448

Week-32-Dream-International-Research-2024-czolgm

Soccer Percentage

Week 32 Pools RSK Papers…. Enjoy your Forecast

Also check: Week 32 Pools SURE banker room

TO VIEW PREVIOUS WEEKS,GO TO WEEKLY POOLS RSK PAPERS ARCHIVE

For live scores updates, see – Livescoresupdate.com

Sir obinna please 🙏 tample of draw paper please help us.god bless you sir

Check back later.

Please,help us with Temple of draws paper, thanks and God bless you

Not Available this week

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