Prediction of football injuries using GPS-based data in Iranian professional football players: a machine learning approach.

Abstract

Introduction: The study aims to assess and compare the predictive effectiveness of football-related injuries using external load data and a decision tree classification algorithm by unidimensional approach.

Methods: The sample consisted of 25 players from one of the 16 teams participating in the Persian Gulf Pro League during the 2022--2023 season. Player injury data and raw GPS data from all training and competition sessions throughout the football league season were gathered (214 training sessions and 34 competition sessions). The acute-tochronic workload ratio was calculated separately for each variable using a ratio of 1:3 weeks. Finally, the decision tree algorithm with machine learning was utilised to assess the predictive power of injury occurrence based on the acute-to-chronic workload ratio.

Results: The results showed that the variable of the number of decelerations had the highest predictive power compared to other variables [area under the curve (AUC) = 0.91, recall = 87.5%, precision = 58.3%, accuracy = 94.7%].

Conclusion: Although none of the selected external load variables in this study had high predictive power (AUC > 0.95), due to the high predictive power of injury of the number of deceleration variables compared with other variables, the necessity of attention and management of this variable as a risk factor for injury occurrence is essential for preventing future injuries.