Sports Injury Risk Prediction

This application area focuses on predicting individual athletes’ risk of specific injuries—such as ACL tears or muscle strains—using historical, biomechanical, training load, and medical data. The goal is to identify who is most likely to get injured and when, so medical and performance staff can intervene proactively with tailored training, load management, and rehabilitation protocols. It also includes automated analysis of movement patterns (e.g., knee kinematics) to detect prior injuries or lingering deficits that may elevate future risk. AI is used to uncover complex, non‑linear relationships between workload, biomechanics, health markers, and injury outcomes that are difficult for humans to detect reliably. Interpretable modeling techniques (e.g., SHAP) make the predictions transparent, highlighting the factors driving risk for each athlete so coaches and clinicians can trust and act on the insights. This moves organizations from intuition‑based decision‑making to data‑driven injury prevention, reducing lost playing time, treatment costs, and career‑impacting events.