  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.
