@Article{Nassis2023,
journal="Biology of Sport",
issn="0860-021X",
volume="40",
number="1",
year="2023",
title="A review of machine learning applications in soccer with an emphasis on injury risk",
abstract="This narrative review paper aimed to discuss the literature on machine learning applications in  soccer with an emphasis on injury risk assessment. A secondary aim was to provide practical tips for the health  and performance staff in soccer clubs on how machine learning can provide a competitive advantage. Performance  analysis is the area with the majority of research so far. Other domains of soccer science and medicine with  machine learning use are injury risk assessment, players’ workload and wellness monitoring, movement analysis,  players’ career trajectory, club performance, and match attendance. Regarding injuries, which is a hot topic,  machine learning does not seem to have a high predictive ability at the moment (models specificity ranged  from 74.2%-97.7%. sensitivity from 15.2%-55.6% with area under the curve of 0.66–0.83). It seems, though,  that machine learning can help to identify the early signs of elevated risk for a musculoskeletal injury. Future  research should account for musculoskeletal injuries’ dynamic nature for machine learning to provide more  meaningful results for practitioners in soccer.",
author="Nassis, George
and Verhagen, Evert
and Brito, João
and Figueiredo, Pedro
and Krustrup, Peter",
pages="233--239",
doi="10.5114/biolsport.2023.114283",
url="http://dx.doi.org/10.5114/biolsport.2023.114283"
}