使用GPS训练数据和机器学习进行有效的足球损伤预测
Effective injury forecasting in soccer with GPS training data and machine learning
摘要：
伤病对职业足球有很大的影响，因为它们对球队的表现有很大的影响，而且球员的康复费用也很高。文献中现有的研究仅提供了对哪些因素主要影响伤害风险的初步了解，而对统计模型在预测伤害方面的潜力的评估仍然缺失。在本文中，我们提出了一个多维的方法，在职业足球的伤害预测是基于GPS测量和机器学习。通过GPS跟踪技术，我们收集了描述职业足球俱乐部球员在一个赛季中的训练负荷的数据。然后，我们构建了一个伤害预测，并通过提供一组足球从业者感兴趣的案例研究表明，它是准确和可解释的。我们的方法开辟了一个新的视角伤害预防，提供了一套简单而实用的规则，评估和解释职业足球受伤风险和训练表现之间的复杂关系。
英文摘要：
Injuries have a great impact on professional soccer, due to their large influence on team performance and the considerable costs of rehabilitation for players. Existing studies in the literature provide just a preliminary understanding of which factors mostly affect injury risk, while an evaluation of the potential of statistical models in forecasting injuries is still missing. In this paper, we propose a multi-dimensional approach to injury forecasting in professional soccer that is based on GPS measurements and machine learning. By using GPS tracking technology, we collect data describing the training workload of players in a professional soccer club during a season. We then construct an injury forecaster and show that it is both accurate and interpretable by providing a set of case studies of interest to soccer practitioners. Our approach opens a novel perspective on injury prevention, providing a set of simple and practical rules for evaluating and interpreting the complex relations between injury risk and training performance in professional soccer.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
