Document - Who is going to get hurt? Predicting injuries in professional soccer

2017

Conference article Open Access

Who is going to get hurt? Predicting injuries in professional soccer

Rossi A., Pappalardo L., Cintia P., Fernandez J., Iaia F. M., Medina D.

sports analytics data science sports science

Injury prevention has a fundamental role in professional soccer due to the high cost of recovery for players and the strong influence of injuries on a club's performance. In this paper we provide a predictive model to prevent injuries of soccer players using a multidimensional approach based on GPS measurements and machine learning. In an evolutive scenario, where a soccer club starts collecting the data for the first time and updates the predictive model as the season goes by, our approach can detect around half of the injuries, allowing the soccer club to save 70% of a season's economic costs related to injuries. The proposed approach can be a valuable support for coaches, helping the soccer club to reduce injury incidence, save money and increase team performance.

Source: MLSA'17 - 4th Workshop on Machine Learning and Data Mining for Sports Analytics, pp. 21–30, Skopje, Macedonia, 18 September 2017

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BibTeX entry

@inproceedings{oai:it.cnr:prodotti:385733,
	title = {Who is going to get hurt? Predicting injuries in professional soccer},
	author = {Rossi A. and Pappalardo L. and Cintia P. and Fernandez J. and Iaia F. M. and Medina D.},
	booktitle = {MLSA'17 - 4th Workshop on Machine Learning and Data Mining for Sports Analytics, pp. 21–30, Skopje, Macedonia, 18 September 2017},
	year = {2017}
}