Predicting Soccer Players' Fitness Status Through a Machine-Learning Approach.

IF 3.5 2区 医学 Q1 PHYSIOLOGY
Mauro Mandorino, Jo Clubb, Mathieu Lacome
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引用次数: 0

Abstract

Purpose: The study had 3 purposes: (1) to develop an index using machine-learning techniques to predict the fitness status of soccer players, (2) to explore the index's validity and its relationship with a submaximal run test (SMFT), and (3) to analyze the impact of weekly training load on the index and SMFT outcomes.

Methods: The study involved 50 players from an Italian professional soccer club. External and internal loads were collected during training sessions. Various machine-learning algorithms were assessed for their ability to predict heart-rate responses during the training drills based on external load data. The fitness index, calculated as the difference between actual and predicted heart rates, was correlated with SMFT outcomes.

Results: Random forest regression (mean absolute error = 3.8 [0.05]) outperformed the other machine-learning algorithms (extreme gradient boosting and linear regression). Average speed, minutes from the start of the training session, and the work:rest ratio were identified as the most important features. The fitness index displayed a very large correlation (r = .70) with SMFT outcomes, with the highest result observed during possession games and physical conditioning exercises. The study revealed that heart-rate responses from SMFT and the fitness index could diverge throughout the season, suggesting different aspects of fitness.

Conclusions: This study introduces an "invisible monitoring" approach to assess soccer player fitness in the training environment. The developed fitness index, in conjunction with traditional fitness tests, provides a comprehensive understanding of player readiness. This research paves the way for practical applications in soccer, enabling personalized training adjustments and injury prevention.

通过机器学习方法预测足球运动员的体能状况
目的:该研究有三个目的:(1) 利用机器学习技术开发一个指数,用于预测足球运动员的体能状况;(2) 探讨该指数的有效性及其与次极限跑测试(SMFT)的关系;(3) 分析每周训练负荷对指数和 SMFT 结果的影响:研究涉及意大利一家职业足球俱乐部的 50 名球员。方法:研究对象为来自意大利职业足球俱乐部的 50 名球员,在训练过程中收集外部和内部负荷。根据外部负荷数据,评估了各种机器学习算法预测训练期间心率反应的能力。以实际心率与预测心率之差计算的体能指数与 SMFT 结果相关:结果:随机森林回归(平均绝对误差 = 3.8 [0.05])优于其他机器学习算法(极端梯度提升和线性回归)。平均速度、训练课开始后的分钟数和工作与休息的比例被认为是最重要的特征。体能指数与 SMFT 结果显示出非常大的相关性(r = .70),在持球游戏和体能调节练习中观察到的结果最高。研究显示,在整个赛季中,SMFT 和体能指数的心率反应可能会出现差异,这表明体能的不同方面:本研究引入了一种 "隐形监测 "方法来评估足球运动员在训练环境中的体能状况。开发的体能指数与传统体能测试相结合,可全面了解球员的准备情况。这项研究为足球运动中的实际应用铺平了道路,使个性化训练调整和伤病预防成为可能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
5.80
自引率
12.10%
发文量
199
审稿时长
6-12 weeks
期刊介绍: The International Journal of Sports Physiology and Performance (IJSPP) focuses on sport physiology and performance and is dedicated to advancing the knowledge of sport and exercise physiologists, sport-performance researchers, and other sport scientists. The journal publishes authoritative peer-reviewed research in sport physiology and related disciplines, with an emphasis on work having direct practical applications in enhancing sport performance in sport physiology and related disciplines. IJSPP publishes 10 issues per year: January, February, March, April, May, July, August, September, October, and November.
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