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Effective injury prediction in professional soccer with GPS data and machine learning

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 multidimensional approach to injury prediction in professional soccer which 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 show that our injury predictors are 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.

Keywords sports analytics · data science · machine learning · sports science · predictive analytics

1 Introduction

Injuries of professional athletes have a great impact on sports industry, due to their large influence on both the mental state and the performance of a team.

Furthermore, the costs associated with the complex process of recovery and rehabilitation for the player is often considerable, both in terms of medical care and missed earnings deriving from the popularity of the player himself. Recent research demonstrates that injuries in Spain cause an average of 16% of season absence by players, corresponding to a total cost estimation of 188 million euros just in one season. It is not surprising hence that injury prediction is attracting a growing interest from researchers, managers, and coaches, who are interested in intervening with appropriate actions to reduce the likelihood of injuries of their players.

Historically, academic work on injury prediction has been deterred for decades by the limited availability of data describing the physical activity of players during the season.

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