Machine Learning Approaches to Injury Risk Prediction in Sport

Injury prediction has emerged as a priority within sports science and performance management to limit athlete downtime and enhance performance results. Machine learning (ML) has transformed the field in the short term by enabling the detection of complex patterns and risk factors from diverse data sources related to athletes. This narrative review provides an overview of the current knowledge on ML methods, key results, and the challenges encountered when applying them to injury risk prediction in various sports. This is among the first review articles to bring together and examine how ML is used for predicting injuries across various sports and different types of models. This review offers practical guidance for sports scientists and clinicians looking to incorporate simple-to-understand ML models into their athlete monitoring and injury prevention efforts. A thorough literature search was conducted using databases such as PubMed, Scopus, and Google Scholar, with keywords including “injury prediction,” “machine learning,” “sports analytics,” and “athlete monitoring.” Tree-based methods, especially Random Forests and XGBoost variants, consistently performed best, effectively managing non-linear and multi-factorial inputs. Deep and hybrid models remain promising, particularly for multi-modal data sets; however, poor interpretability constraints limit their widespread use. Training load, sleep quality, and previous injury status were key predictive features. However, unbalanced datasets, inconsistent injury definitions, and broad prediction windows further undermine these present attempts in terms of generalizability and clinical relevance. Although accuracy measures reported by some models are very high, their utility in real-world settings is limited due to small sample sizes and high method heterogeneity. Designated data protocols need to be instituted; the explainability of the model in injury risk prediction must be enhanced; and ethical frameworks for data use must be established, considering the practical application of ML.