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

George P. Nassis, Evert Verhagen, João Brito, Pedro Figueiredo, Peter Krustrup

Research output: Contribution to journalReview articlepeer-review

15 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)233-239
Number of pages7
JournalBiology of Sport
Volume40
Issue number1
DOIs
Publication statusPublished - 2023

Keywords

  • Big data
  • Data analytics
  • Football
  • Machine learning
  • Soccer injury risk

ASJC Scopus subject areas

  • Orthopedics and Sports Medicine
  • Physical Therapy, Sports Therapy and Rehabilitation
  • Physiology (medical)

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