Injuries in elite sports disrupt team performance, shorten careers, and incur significant financial costs, highlighting the critical need for accurate predictions to inform optimal decisions that effectively prevent injuries. Existing approaches to injury prediction fail to account for cumulative risk, overlook injury severity, lack reliable probability calibration, and omit statistically guided decision thresholds. Here, we present a novel injury prediction framework integrating risk accumulation via survival analysis with machine learning, probability beta calibration, and statistical decision theory. Using a unique dataset spanning four seasons from FC Barcelona’s women’s team, we demonstrate that our framework outperforms standard classifiers, yielding superior discrimination ability. Our framework identifies fatigue-related measures as key injury predictors and incorporates flexible thresholds based on match importance and decision-maker certainty, improving player availability. Scalable and transferable to other sports, this framework bridges academic research and practical deployment, empowering sports organizations to optimize player performance and long-term outcomes.

E.F, and G.R. are employed by Barca Innovation Hub. J.R.G. serves as a scientific advisor to Made of Genes, for which he receives financial compensation. B.C. is pursuing an industrial PhD in collaboration with ISGlobal and Made of Genes. All other authors have no competing interests to declare.

This study was funded by the Ministry of Research and Universities, Government of Catalonia, under reference AGAUR 2023 PROD 00020. It also received financial support by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG) under Germany's Excellence Strategy (EXC 2047 - 390685813 and EXC 2151 - 390873048), the University of Bonn (via the Schlegel Professorship of JH), and by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council Executive Agency. Neither the European Union nor the granting authority can be held responsible for them. This work is supported by ERC grant INTEGRATE, grant agreement number 101126146. The funders had no role in the study design, data collection, data analyses, data interpretation, writing, or submission of this manuscript.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

This study adhered to the Declaration of Helsinki guidelines and received approval from both the Barca Innovation Hub's local committee and the Ethics Committee of Consell Catala de l'Esport (code 012/CEICGC/2021). Participants were informed about the study's potential risks and benefits. All personal data and results were anonymized to maintain confidentiality and comply with the criteria specified by the General Data Protection Regulation (GDPR).

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Data will be available upon reasonable request, considering ethical and privacy concerns, from the author Gil Rodas.