%0 Generic %1 rossi2017effective %A Rossi, Alessio %A Pappalardo, Luca %A Cintia, Paolo %A Iaia, Marcello %A Fernandez, Javier %A Medina, Daniel %D 2017 %J ArXiv e-prints %K complexsystems datascience machinelearning sportsanalytics sportscience %T Effective injury prediction in professional soccer with GPS data and machine learning %U https://arxiv.org/abs/1705.08079 %X 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.

@preprint{rossi2017effective, 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.}, added-at = {2017-05-25T17:43:40.000+0200}, author = {Rossi, Alessio and Pappalardo, Luca and Cintia, Paolo and Iaia, Marcello and Fernandez, Javier and Medina, Daniel}, biburl = {https://www.bibsonomy.org/bibtex/27da9af05d3fbc02a64b47046cbca9d3a/lucapappalardo}, interhash = {777c334b4b3b6972003e0285074393a9}, intrahash = {7da9af05d3fbc02a64b47046cbca9d3a}, journal = {ArXiv e-prints}, key = {Statistics - Machine Learning, Computer Science - Artificial Intelligence, Statistics - Applications, 62-07, H.2.8}, keywords = {complexsystems datascience machinelearning sportsanalytics sportscience}, month = may, timestamp = {2017-05-25T17:50:49.000+0200}, title = {Effective injury prediction in professional soccer with GPS data and machine learning}, url = {https://arxiv.org/abs/1705.08079}, year = 2017 }