Scholarlink

<p>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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Injury prediction in elite women’s football: an integrative machine learning-based decision-support framework

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月8日

查看全文
  • Manuel Huth,
  • Berta Canal-Simón,
  • Eva Ferrer,
  • Gil Rodas,
  • Xavier Yanguas,
  • Jan Hasenauer,
  • Juan R. González

摘要

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.