{
  "abstract": "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.",
  "abstract_shown": true,
  "affiliations": [
    "Life and Medical Sciences (LIMES) Institute, University of Bonn, Bonn, Germany",
    "Bonn Center for Mathematical Life Sciences, University of Bonn, Bonn, Germany",
    "Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain",
    "Made of Genes, Barcelona, Spain",
    "Medical Department of Football Club Barcelona (FIFA Medical Centre of Excellence), and Barça Innovation Hub of Football Club Barcelona, Barcelona, Spain",
    "Sports and Exercise Medicine Unit, Hospital Clinic and Sant Joan de Déu, Barcelona, Spain",
    "Leitat technological Center, Terrassa, Spain",
    "CIBER in Epidemiology and Public Health (CIBERESP), Barcelona, Spain",
    "Department of Mathematics, Universitat Autònoma de Barcelona (UAB), Barcelona, Spain"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Manuel Huth"
    },
    {
      "affs": [
        2,
        3
      ],
      "name": "Berta Canal-Simón"
    },
    {
      "affs": [
        4,
        5
      ],
      "name": "Eva Ferrer"
    },
    {
      "affs": [
        4,
        5,
        6
      ],
      "name": "Gil Rodas"
    },
    {
      "affs": [
        4
      ],
      "name": "Xavier Yanguas"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Jan Hasenauer"
    },
    {
      "affs": [
        2,
        7,
        8
      ],
      "name": "Juan R. González"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6z15h3xt5abkm.bib",
  "cited_by": 1,
  "doi": "10.1038/s41746-026-02937-3",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13724727/",
  "id": "6z15h3xt5abkm",
  "issns": [
    "2398-6352"
  ],
  "landing_url": "https://doi.org/10.1038/s41746-026-02937-3",
  "language": "en",
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Article"
    ],
    [
      "openalex",
      "2732|Sports injuries and prevention"
    ]
  ],
  "openalex": "W7167690861",
  "pdf_url": "https://www.nature.com/articles/s41746-026-02937-3_reference.pdf",
  "pmcid": "PMC13724727",
  "pmid": "42420477",
  "published": "2026-07-08",
  "publisher": "Nature Publishing Group",
  "ris": "https://research.draconitic.com/paper/6z15h3xt5abkm.ris",
  "sources": [
    {
      "first_seen": "2026-11-04T12:23:15Z",
      "native_id": "PMC13724727",
      "source": "pmc",
      "url": "https://doi.org/10.1038/s41746-026-02937-3"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2732|Sports injuries and prevention",
      "subfield": "surgery"
    }
  ],
  "title": "Injury prediction in elite women’s football: an integrative machine learning-based decision-support framework",
  "title_text": "Injury prediction in elite women’s football: an integrative machine learning-based decision-support framework",
  "type": "peer-reviewed",
  "updated_at": "2026-11-06T12:21:08Z",
  "url": "https://research.draconitic.com/paper/6z15h3xt5abkm",
  "venue": "NPJ Digital Medicine",
  "versions": []
}