Beyond the algorithm: A qualitative study of expert perceptions on the impact of injury prediction models in men's professional football.

Authors

Francisco Martins, Élvio Rúbio Gouveia, Krzysztof Przednowek, Honorato Sousa, Hugo Sarmento

Published in

Journal of science and medicine in sport. Sep 21, 2026. Epub Sep 21, 2026.

Abstract

The potential of machine learning to predict injury risk in men's professional football has been explored in quantitative studies, yet the perceptions and experiences of performance staff who integrate these technologies into daily practice remain underexplored.
This qualitative investigation aimed to explore performance professionals' perceptions of the practical utility, implementation barriers, and role in decision-making of muscle injury prediction models in men's professional football.
Semi-structured interviews were conducted with 10 male performance staff members (4 physical trainers, 3 physiologists, 3 heads of performance; mean experience: 11.0 ± 5.0 years) working in men's professional football.
Data were analyzed using a deductive-inductive thematic approach.
The analysis identified three main themes: (i) determinants of prevention and injury risk profile, (ii) strategies for assessing and monitoring injury risk, and (iii) applicability and impact of injury prediction models. Participants described prediction models as decision-support tools whose usefulness depended on integration with clinical judgment, longitudinal monitoring, and organizational context. Implementation was constrained by financial and structural limitations and required translating complex data into actionable information for coaches. High-risk situations required shared negotiation among staff and players rather than unilateral decisions based on algorithmic outputs.
In men's professional football, injury prediction models were perceived less as a replacement for human judgment and more as a complement to multidisciplinary decision-making. As the sample comprised exclusively male professionals in men's football, transferability to women's football and female professionals should be examined in future research.

PMID:
42810916
Bibliographic data and abstract were imported from PubMed on 30 Sep 2026.

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