{"title":"通过机器学习方法预测足球运动员的体能状况","authors":"Mauro Mandorino, Jo Clubb, Mathieu Lacome","doi":"10.1123/ijspp.2023-0444","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":14295,"journal":{"name":"International journal of sports physiology and performance","volume":" ","pages":"443-453"},"PeriodicalIF":3.5000,"publicationDate":"2024-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Predicting Soccer Players' Fitness Status Through a Machine-Learning Approach.\",\"authors\":\"Mauro Mandorino, Jo Clubb, Mathieu Lacome\",\"doi\":\"10.1123/ijspp.2023-0444\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>\",\"PeriodicalId\":14295,\"journal\":{\"name\":\"International journal of sports physiology and performance\",\"volume\":\" \",\"pages\":\"443-453\"},\"PeriodicalIF\":3.5000,\"publicationDate\":\"2024-02-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International journal of sports physiology and performance\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1123/ijspp.2023-0444\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/5/1 0:00:00\",\"PubModel\":\"Print\",\"JCR\":\"Q1\",\"JCRName\":\"PHYSIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International journal of sports physiology and performance","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1123/ijspp.2023-0444","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/5/1 0:00:00","PubModel":"Print","JCR":"Q1","JCRName":"PHYSIOLOGY","Score":null,"Total":0}
Predicting Soccer Players' Fitness Status Through a Machine-Learning Approach.
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.
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.