Global Positioning System-Derived Metrics and Machine Learning Models for Injury Prediction in Professional Rugby Union Players.

ABSTRACT: In sports, injury prevention is a key factor for success. Although injuries are challenging to predict, new technologies and the application of data science can provide valuable insights. This study aimed to predict injury risk among professional rugby union players using machine learning (ML) models. We analyzed data from 63 professional rugby union players during three seasons, categorized them into forwards and backs, and further classified them into five specific positions (tight five, back row, scrum-half, inside backs, outside backs). The dataset included GPS data and derived metrics such as total workload in the 1, 2, and 3 weeks prior to injury, acute-to-chronic workload ratio over different time windows, monotony, and strain. Injury prediction was assessed separately for different

SUBMITTER: Ren X 

PROVIDER: S-EPMC12460712 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature