Abstract
Background: Professional football requires more attention in planning work regimens that balance players' sports performance optimization and reduce their injury probability. Machine learning applied to sports science has focused on predicting these events and identifying their risk factors. Our study aims to (i) analyze the differences between injury incidence during training and matches and (ii) build and classify different predictive models of risk based on players' internal and external loads across four sports seasons. Methods: This investigation involved 96 male football players (26.2 ± 4.2 years; 181.1 ± 6.1 cm; 74.5 ± 7.1 kg) representing a single professional football club across four analyzed seasons. The research was designed according to three methodological sets of assessments: (i) average season performance, (ii) two weeks' performance before the event, and (iii) four weeks' performance before the event. We applied machine learning classification methods to build and classify different predictive injury risk models for each dataset. The dependent variable is categorical, representing the occurrence of a time-loss muscle injury (N = 97). The independent variables include players' information and external (GPS-derived) and internal (RPE) workload variables. Results: The Kstar classifier with the four-week window dataset achieved the best predictive performance, presenting an Area Under the Precision-Recall Curve (AUC-PR) of 83% and a balanced accuracy of 72%. Conclusions: In practical terms, this methodology provides technical staff with more reliable data to inform modifications to playing and training regimens. Future research should focus on understanding the technical staff's qualitative vision of predictive models' in-field applicability.