Predictive models of injury risk in male professional football players: a systematic review.

Background: One of the challenges for professional football players is injuries. Due to their influence on their teams, injuries greatly impact the sports business. This research aims to assess predictive models of injury risk in male professional football players.

Methods: A systematic literature review was performed, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The search was conducted in the PubMed, Web of Science and Scopus databases. Two independent reviewers screened articles, assessed eligibility and extracted data. Methodological quality was determined by the Newcastle-Ottawa Scale.

Results: 26 studies met the inclusion criteria.

Discussion: Various statistical techniques were used in research on injury prediction in professional football, with logistic regression being the most used. The assessment predictors, especially the area under the receiver operating characteristic Curve, showed significant variation, which indicates the prediction models' efficacy. The focus was frequently on lower limb injuries, where several risk predictors, including muscular strength, flexibility and global positioning system-derived data, were found to substantially impact the occurrence of injuries. Prominent predictors included age, position, physiological parameters, injury history and genetic polymorphisms.

Conclusions: This comprehensive analysis highlights the complexity of injury prediction and reinforces the necessity for football injury research to adopt a multivariate approach with accuracy and comprehensiveness.

Prospero registration number: CRD42023465524.