B1139
Title: Injury forecasting in sports using artificial intelligence
Authors: Luca Pappalardo - ISTI-CNR (Italy) [presenting]
Alessio Rossi - University of Pisa (Italy)
Paolo Cintia - University of Pisa (Italy)
Abstract: Injuries have a great impact on professional athletes and clubs, due to their large influence on team performance and the considerable costs of rehabilitation for players. Existing studies in the literature provide just a preliminary understanding of which factors mostly affect injury risk, while an evaluation of the potential of statistical models in forecasting injuries is still missing. We propose an approach to injury forecasting in professional sports that is based on artificial intelligence. By using GPS tracking technology, we collect data describing the training workload of players in a professional soccer club during two seasons. We then construct an injury forecaster and show that its accuracy outperforms existing methods for injury risk assessment currently used by professional clubs. Our approach opens a novel perspective on injury prevention, providing a set of simple and practical rules for evaluating and interpreting the complex relations between injury risk and training performance in professional sports.
