{"title":"Injury Prediction for Soccer Players Using Machine Learning","authors":"Amiel Satvedi, Richard Pyne","volume":183,"journal":"International Journal of Sport and Health Sciences","pagesStart":21,"pagesEnd":28,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/10012426","abstract":"
Injuries in professional sports occur on a regular basis. Some may be minor while others can cause huge impact on a player\u2019s career and earning potential. In soccer, there is a high risk of players picking up injuries during game time. This research work seeks to help soccer players reduce the risk of getting injured by predicting the likelihood of injury while playing in the near future and then providing recommendations for intervention. The injury prediction tool will use a soccer player\u2019s number of minutes played on the field, number of appearances, distance covered and performance data for the current and previous seasons as variables to conduct statistical analysis and provide injury predictive results using a machine learning linear regression model.<\/p>","references":"[1]\tB. Alamar, Sports analytics: a guide for coaches, managers, and other decision makers. New York: Columbia University Press, 2013. \r\n[2]\tE. 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