Effective injury forecasting in soccer with GPS training data and machine learning

TL;DR

This paper presents a novel injury forecasting method in professional soccer using GPS training data and machine learning, offering accurate, interpretable predictions to aid injury prevention.

Contribution

It introduces a multi-dimensional approach combining GPS data and machine learning for injury prediction, filling a gap in injury forecasting research.

Findings

The injury forecaster is both accurate and interpretable.

GPS data effectively predicts injury risk.

Provides practical rules for injury risk evaluation.

Abstract

Injuries have a great impact on professional soccer, 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. In this paper, we propose a multi-dimensional approach to injury forecasting in professional soccer that is based on GPS measurements and machine learning. By using GPS tracking technology, we collect data describing the training workload of players in a professional soccer club during a season. We then construct an injury forecaster and show that it is both accurate and interpretable by providing a set of case studies of interest to soccer practitioners. 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 soccer.