Abstract
Substitution is an essential tool for a coach to influence the match. Factors like the injury of a player, required tactical changes, or underperformance of a player initiates substitutions. This study aims to predict the physical performance of individual players in an early phase of the match to provide additional information to the coach for his decision on substitutions. Tracking data of individual players, except for goalkeepers, from 302 elite soccer matches of the Dutch ‘Eredivisie’ 2018–2019 season were used to enable the prediction of the individual physical performance. The players’ physical performance is expressed in the variables distance covered, distance in speed category, and energy expenditure in power category. The individualized normalized variables were used to build machine learning models that predict whether players will achieve 100%, 95%, or 90% of their average physical performance in a match. The tree-based algorithms Random Forest and Decision Tree were applied to build the models. A simple Naïve Bayes algorithm was used as the baseline model to support the superiority of the tree-based algorithms. The machine learning technique Random Forest combined with the variable energy expenditure in the power category was the most precise. The combination of Random Forest and energy expenditure in the power category resulted in precision in predicting performance and underperformance after 15 min in a match, and the values were 0.91, 0.88, and 0.92 for the thresholds 100%, 95%, and 90%, respectively. To conclude, it is possible to predict the physical performance of individual players in an early phase of the match. These findings offer opportunities to support coaches in making more informed decisions on player substitutions in elite soccer.
Highlights
Soccer is a highly competitive and physically demanding sport
The energy expenditure in the power category shows a decline in the average energy expenditure in the power categories IP from 7.26 ± 1.24 kJ·kg−1 in the first half to 6.47 ± 1.14 kJ·kg−1 in the second half (p < 0.001, ε2 = 0.75), HP from
In line with previous research, this study revealed that entire‐match players physical performance within the match, making the identification underperforming show a significant decline in physical performance during theof match in distance covered, distance covered in the speed category, and energy expenditure in the that power category players critical points
Summary
Soccer is a highly competitive and physically demanding sport. The physical demand is highlighted by an increase in ball (game) speed by 15% over the last 50 years [1]. In the Italian A series, a team showed a significant reduction between the first and second half in high-intensity running distance (−14.9%) [3]. These examples highlight that players are unable to perform maximally throughout a match [4]. Information on this drop in performance is essential for players and coaches. Teams and coaches need to identify players that physically underperform in a match as early as possible to adapt their style of play or substitute these players. An injury of a player, necessary tactical changes, or underperformance of a player causes substitutions
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