Predicting Soccer Players’ Fitness Status Through a Machine-Learning Approach

机器学习 人工智能 计算机科学 比赛比赛 物理医学与康复 物理疗法 医学
作者
Mauro Mandorino,Jo Clubb,Mathieu Lacome
出处
期刊:International Journal of Sports Physiology and Performance [Human Kinetics]
卷期号:19 (5): 443-453 被引量:5
标识
DOI:10.1123/ijspp.2023-0444
摘要

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.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
刚刚
4秒前
4秒前
菜籽发布了新的文献求助10
4秒前
辛辛完成签到,获得积分10
5秒前
7秒前
温婉的靖儿完成签到,获得积分10
7秒前
充电宝应助elle采纳,获得10
7秒前
ll发布了新的文献求助30
7秒前
Shining_Wu发布了新的文献求助10
10秒前
研友_VZG7GZ应助jianhua采纳,获得10
10秒前
深情安青应助青4096采纳,获得20
11秒前
跪求发布了新的文献求助10
13秒前
科研通AI5应助LLLLLLL采纳,获得10
13秒前
14秒前
饱满若灵发布了新的文献求助10
16秒前
17秒前
18秒前
hkh发布了新的文献求助10
18秒前
傅英俊完成签到,获得积分10
20秒前
充电宝应助lw采纳,获得10
20秒前
xie完成签到,获得积分10
21秒前
孙不缺发布了新的文献求助10
23秒前
勿奈何完成签到,获得积分10
24秒前
24秒前
biopharm2000发布了新的文献求助30
24秒前
25秒前
25秒前
wangwangwang123完成签到,获得积分10
26秒前
26秒前
高文强完成签到 ,获得积分10
27秒前
lw完成签到,获得积分10
27秒前
27秒前
菜籽完成签到,获得积分10
29秒前
29秒前
醒醒发布了新的文献求助10
31秒前
31秒前
31秒前
青4096发布了新的文献求助20
32秒前
32秒前
高分求助中
【此为提示信息,请勿应助】请按要求发布求助,避免被关 20000
Encyclopedia of Geology (2nd Edition) 2000
Maneuvering of a Damaged Navy Combatant 650
Периодизация спортивной тренировки. Общая теория и её практическое применение 310
Mixing the elements of mass customisation 300
the MD Anderson Surgical Oncology Manual, Seventh Edition 300
Nucleophilic substitution in azasydnone-modified dinitroanisoles 300
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 物理 生物化学 纳米技术 计算机科学 化学工程 内科学 复合材料 物理化学 电极 遗传学 量子力学 基因 冶金 催化作用
热门帖子
关注 科研通微信公众号,转发送积分 3780028
求助须知:如何正确求助?哪些是违规求助? 3325388
关于积分的说明 10222846
捐赠科研通 3040559
什么是DOI,文献DOI怎么找? 1668897
邀请新用户注册赠送积分活动 798857
科研通“疑难数据库(出版商)”最低求助积分说明 758612