I am excited to share our new publication in the International Journal of Sports Physiology and Performance! We wanted to explore the 'invisible monitoring' approach further by using a machine learning approach to build a fitness index from small sided game football data.
Purpose: The study had 3 purposes: (1) to develop an index using machine-learning techniques to predict the fitness status of soccer players, (2) to explore the index’s validity and its relationship with a submaximal run test (SMFT), and (3) to analyze the impact of weekly training load on the index and SMFT outcomes.
Methods: The study involved 50 players from an Italian professional soccer club. External and internal loads were collected during training sessions. Various machine-learning algorithms were assessed for their ability to predict heart-rate responses during the training drills based on external load data. The fitness index, calculated as the difference between actual and predicted heart rates, was correlated with SMFT outcomes.
Results: Random forest regression (mean absolute error = 3.8 [0.05]) outperformed the other machine-learning algorithms (extreme gradient boosting and linear regression). Average speed, minutes from the start of the training session, and the work:rest ratio were identified as the most important features. The fitness index displayed a very large correlation (r = .70) with SMFT outcomes, with the highest result observed during possession games and physical conditioning exercises. The study revealed that heart-rate responses from SMFT and the fitness index could diverge throughout the season, suggesting different aspects of fitness.
Conclusions: This study introduces an “invisible monitoring” approach to assess soccer player fitness in the training environment. The developed fitness index, in conjunction with traditional fitness tests, provides a comprehensive understanding of player readiness. This research paves the way for practical applications in soccer, enabling personalized training adjustments and injury prevention.
It was a pleasure to collaborate with Mauro Mandorino and Mathieu Lacome on this paper.
Mandorino, M., Clubb, J., & Lacome, M. (2024). Predicting Soccer Players’ Fitness Status Through a Machine-Learning Approach. International Journal of Sports Physiology and Performance, 1(aop), 1-11.
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