Injury Prediction for Soccer Players Using Machine Learning

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Players Using Machine Learning</title> <meta name="description" content="Injury Prediction for Soccer Players Using Machine Learning"> <meta name="keywords" content="Injury predictor, soccer injury prevention, machine learning in soccer, big data in soccer."> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <meta name="citation_title" content="Injury Prediction for Soccer Players Using Machine Learning"> <meta name="citation_author" content="Amiel Satvedi"> <meta name="citation_author" content="Richard Pyne"> <meta name="citation_publication_date" content="2022/03/02"> <meta name="citation_journal_title" content="International Journal of Sport and Health Sciences"> <meta name="citation_volume" content="16"> <meta name="citation_issue" content="3"> <meta name="citation_firstpage" content="21"> <meta name="citation_lastpage" content="27"> <meta name="citation_pdf_url" 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class="card-text"><strong>Abstract:</strong></p> <p>Injuries in professional sports occur on a regular basis. Some may be minor while others can cause huge impact on a player’s 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’s 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> <iframe src="https://publications.waset.org/10012426.pdf" style="width:100%; height:400px;" frameborder="0"></iframe> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Injury%20predictor" title="Injury predictor">Injury predictor</a>, <a href="https://publications.waset.org/search?q=soccer%20injury%20prevention" title=" soccer injury prevention"> soccer injury prevention</a>, <a href="https://publications.waset.org/search?q=machine%20learning%20in%20soccer" title=" machine learning in soccer"> machine learning in soccer</a>, <a href="https://publications.waset.org/search?q=big%20data%20in%20soccer." title=" big data in soccer."> big data in soccer.</a> </p> <a href="https://publications.waset.org/10012426/injury-prediction-for-soccer-players-using-machine-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10012426/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10012426/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10012426/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10012426/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10012426/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10012426/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10012426/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10012426/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10012426/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10012426/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10012426.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1748</span> </span> <p class="card-text"><strong>References:</strong></p> <br>[1] B. Alamar, Sports analytics: a guide for coaches, managers, and other decision makers. New York: Columbia University Press, 2013. <br>[2] E. Alpaydın, Introduction to machine learning second edition. Cambridge, MA: The MIT Press, 2014. <br>[3] R. J. Bar and J. F. X. DeSouza, “Tracking Plasticity: Effects of Long-Term Rehearsal in Expert Dancers Encoding Music to Movement,” PlosOne, 29-Jan-2016. <br>[4] F. Wunderlich and D. Memmert, “The Betting Odds Rating System: Using soccer forecasts to forecast soccer,” PlosOne, 05-June-2018. <br>[5] K. Pelechrinis and E. Papalexakis, “The Anatomy of American Football: Evidence from 7 Years of NFL Game Data,” PlosOne, 22-Dec-2016. <br>[6] M. Bush, C. Barnes, D. T. Archer, B. Hogg, and P. S. Bradley, “Evolution of match performance parameters for various playing positions in the English Premier League,” ScienceDirect, Feb-2015. <br>[7] B. Pang, L. Lee, and S. Vithyanathan, “Thumbs up? 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Boca Raton, Florida: CRC Press. <br>[13] A. Heuer and O. Rubner, “Optimizing the Prediction Process: From Statistical Concepts to the Case Study of Soccer,” PlosOne, 08-Sept-2014. <br>[14] T. Nishimoto, K. Mukaigawa, S. Tominaga, N. Lubbe, T. Kiuchi, T. Motomura, and H. Matsumoto, “Serious injury prediction algorithm based on large-scale data and under-triage control,” ScienceDirect, Jan 2017. <br>[15] Nationwide Children's Hospital. “Injuries to High School Baseball Players Becoming More Serious,” ScienceDaily, 03-Jun-2008. <br>[16] J. Kang and D.-S. E. Joonbeom Lee, “Smartphone-Based Traveled Distance Estimation Using Individual Walking Patterns for Indoor Localization,” American Journal of Drug and Alcohol Abuse, 18-Sep-2018. <br>[17] O.-S. Kwon and D. C. Knill, “The brain uses adaptive internal models of scene statistics for sensorimotor estimation and planning,” PNAS, 12-Mar-2013. <br>[18] C. Lago-Penas, J. Lago-Ballesteros, A. Dellal, and M. Gomez, “Game-Related Statistics that Discriminated Winning, Drawing and Losing Teams from the Spanish Soccer League,” Journal of Sports Science and Medicine, 01-June-2010. <br>[19] G. D. Myer, K. R. Ford, J. Khoury, P. Succop, and T. E. Hewett, “Biomechanics laboratory-based prediction algorithm to identify female athletes with high knee loads that increase risk of ACL injury,” British Journal of Sports Medicine, 17-June-2010. <br>[20] D. Memmert and R. Rein, “Match Analysis, Big Data and Tactics: Current Trends in Elite Soccer,” Research Gate, March-2018. <br>[21] D. Memmert and J. Perl, “Game creativity analysis using neural networks,” Journal for Sports Sciences, 19-Jan-2009. <br>[22] AFS Enterprises. (2020). English Premier League Statistics of Player’s Age filtered by club (Data File). Retrieved from https://www.11v11.com/teams/arsenal/tab/stats/option/oldest/ <br>[23] Transfermarkt GmbH & Co. KG. (2020). English Premier League Statistics of Player’s Injury History (Data File). 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Injury Prediction for Soccer Players Using Machine Learning