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.

中文翻译：

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精英足球比赛中身体表现的早期预测——一种支持换人的机器学习方法

换人是教练影响比赛的重要工具。球员受伤、需要的战术改变或球员表现不佳等因素会导致换人。本研究旨在预测个别球员在比赛早期的身体表现，以便为教练决定换人提供额外的信息。除守门员外，来自荷兰“荷甲”2018-2019 赛季 302 场精英足球比赛的单个球员的跟踪数据用于预测个人身体表现。运动员的身体表现以距离、速度类别中的距离和功率类别中的能量消耗等变量来表示。个性化的归一化变量用于构建机器学习模型，以预测球员是否会在比赛中达到其平均身体表现的 100%、95% 或 90%。应用基于树的算法随机森林和决策树来构建模型。一个简单的朴素贝叶斯算法被用作基线模型，以支持基于树的算法的优越性。机器学习技术随机森林结合功率类别中的可变能量消耗是最精确的。随机森林和能量消耗在功率类别中的组合可以精确预测比赛 15 分钟后的表现和表现不佳，阈值 100%、95% 和 90% 的值分别为 0.91、0.88 和 0.92，分别。总结一下，可以在比赛的早期预测单个球员的身体表现。这些发现为支持教练在精英足球中对球员换人做出更明智的决定提供了机会。