Revolutionizing Soccer Injury Management: Predicting Muscle Injury Recovery Time Using ML

Abstract

Predicting the optimal recovery time following a soccer player’s injury is a complex task with heavy implications on team performance. While most current decision-based models rely on the physician’s perspective, this study proposes a machine learning (ML)-based approach to predict recovery duration using three modeling techniques: linear regression, decision tree, and extreme gradient boosting (XGB). Performance is compared between the models, against the expert, and together with the expert. The results demonstrate that integrating the expert’s predictions as a feature improves the performance of all models, with XGB performing best with a mean R2 score of 0.72, outperforming the expert’s predictions with an R2 score of 0.62. This approach has significant implications for sports medicine, as it could help teams make better decisions on the return-to-play of their players, leading to improved performance and reduced risk of re-injury.

Keywords

• return-to-play
• machine learning
• recovery estimation
• soccer injuries