Machine learning framework for predicting athletic injuries and optimising performance

This paper describes an interpretable machine learning model to assess the risk of injury among multi-sport college athletes at the university level based on a publicly available collegiate dataset. The variables to be included in the dataset are workload, recovery, performance, and demographic factors of 200 athletes that represent various sports activities. A set of cross-validated models based on supervised learning such as Random Forest, XGBoost, and Artificial Neural Networks were trained with the stratified cross-validation, and the work of the Random Forest has shown the best performance (accuracy = 0.98; ROC-AUC = 0.97). Preprocessing involved scaling of features, categorical encoding, and inspection of outliers and no imputation was needed because all the data is available. Since explainable Artificial Intelligence (XAI) methods, such as SHAP, were incorporated to help understand the model behaviour. The importance of features was shown to be greatest in ACL risk score, load balance score, fatigue score, and training hours, which suggests that the sports injury is multi-factorial in nature. The results point to early-warning indicators bankable on routine workload-recovery balance monitoring as opposed to the use of expensive wearable technology. This structure offers a pragmatic, replicable, and understandable model of injury prediction that could guide coaches and the practitioners in creating decisions based on information. Future directions in line with real-time monitoring and federated learning and external validation of more extensive athletic populations should be studied as future work.