A review of machine learning applications in soccer with an emphasis on injury risk

George Nassis; Evert Verhagen; João Brito; Pedro Figueiredo; Peter Krustrup

doi:10.5114/biolsport.2023.114283

eISSN: 2083-1862
ISSN: 0860-021X

Biology of Sport

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1/2023
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Review paper

A review of machine learning applications in soccer with an emphasis on injury risk

George P. Nassis

^{1, 2}

,

Evert Verhagen

³

,

João Brito

⁴

,

Pedro Figueiredo

^{4, 5}

,

Peter Krustrup

^{2, 6, 7}

Physical Education Department, College of Education, United Arab Emirates University, Al Ain, Abu Dhabi, United Arab Emirates
Department of Sports Science and Clinical Biomechanics, SDU Sport and Health Sciences Cluster (SHSC), University of Southern Denmark, Odense, Denmark
Amsterdam Collaboration on Health and Safety in Sports, Department of Public and Occupational Health, Amsterdam UMC, Amsterdam Movement Sciences, Amsterdam, Netherlands
Portugal Football School, Portuguese Football Federation, Oeiras, Portugal
CIDEFES, Universidade Lusófona, Lisboa, Portugal
Danish Institute for Advanced Study (DIAS), University of Southern Denmark, Odense, Denmark
Sport and Health Sciences, College of Life and Environmental Sciences, University of Exeter, United Kingdom

Biol Sport. 2023;40(1):233–239

DOI: https://doi.org/10.5114/biolsport.2023.114283

Online publish date: 2022/03/16

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Copyright: Institute of Sport. This is an Open Access article distributed under the terms of the Creative Commons CC BY License (https://creativecommons.org/licenses/by/4.0/). This license enables reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use.

A review of machine learning applications in soccer with an emphasis on injury risk

George P. Nassis 1, 2 , Evert Verhagen 3 , João Brito 4 , Pedro Figueiredo 4, 5 , Peter Krustrup 2, 6, 7

George P. Nassis

^{1, 2}

,

Evert Verhagen

³

,

João Brito

⁴

,

Pedro Figueiredo

^{4, 5}

,

Peter Krustrup

^{2, 6, 7}