Diego Marqués-Jiménez, Javier Raya-González, Silvia Sánchez-Díaz, Daniel Castillo
{"title":"采用随机森林聚类方法探讨青少年篮球运动员体能水平对比赛相关体能表现的影响","authors":"Diego Marqués-Jiménez, Javier Raya-González, Silvia Sánchez-Díaz, Daniel Castillo","doi":"10.1177/17543371231200056","DOIUrl":null,"url":null,"abstract":"This study aimed to analyse the influence of different physical fitness levels of youth basketball players on match-related physical performance, using Random Forest clustering to distinguish between high-fitness level players and low-fitness level players. Twenty male youth basketball players completed the following physical performance tests in two separate sessions: bilateral and unilateral countermovement jumps, bilateral and unilateral horizontal jumps, single leg lateral jumps, the 20 m linear straight sprint test, the 505 test and a repeated sprint ability test. 1 week after the second testing day, players completed a simulated match while external loads were monitored using an ultra-wide band-based Local Positioning System. A Random Forest clustering was used to create two different clusters composed of players with similar physical fitness attributes (high- and low-fitness level players). Results indicate that the Random Forest clustering adequately discriminated among the players in different groups according to their physical fitness attributes. High-fitness level players covered more distance per min in all intensity thresholds and reached higher maximal speed and acceleration intensity during the simulated matches ( p < 0.05). These results may assist basketball practitioners in understanding running performance variations during matches and can be used to optimise preparation for individual players.","PeriodicalId":20674,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology","volume":"2012 1","pages":"0"},"PeriodicalIF":1.1000,"publicationDate":"2023-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Random Forest clustering to explore the influence of physical fitness level of youth basketball players on match-related physical performance\",\"authors\":\"Diego Marqués-Jiménez, Javier Raya-González, Silvia Sánchez-Díaz, Daniel Castillo\",\"doi\":\"10.1177/17543371231200056\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This study aimed to analyse the influence of different physical fitness levels of youth basketball players on match-related physical performance, using Random Forest clustering to distinguish between high-fitness level players and low-fitness level players. Twenty male youth basketball players completed the following physical performance tests in two separate sessions: bilateral and unilateral countermovement jumps, bilateral and unilateral horizontal jumps, single leg lateral jumps, the 20 m linear straight sprint test, the 505 test and a repeated sprint ability test. 1 week after the second testing day, players completed a simulated match while external loads were monitored using an ultra-wide band-based Local Positioning System. A Random Forest clustering was used to create two different clusters composed of players with similar physical fitness attributes (high- and low-fitness level players). Results indicate that the Random Forest clustering adequately discriminated among the players in different groups according to their physical fitness attributes. High-fitness level players covered more distance per min in all intensity thresholds and reached higher maximal speed and acceleration intensity during the simulated matches ( p < 0.05). These results may assist basketball practitioners in understanding running performance variations during matches and can be used to optimise preparation for individual players.\",\"PeriodicalId\":20674,\"journal\":{\"name\":\"Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology\",\"volume\":\"2012 1\",\"pages\":\"0\"},\"PeriodicalIF\":1.1000,\"publicationDate\":\"2023-09-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1177/17543371231200056\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ENGINEERING, MECHANICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/17543371231200056","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, MECHANICAL","Score":null,"Total":0}
A Random Forest clustering to explore the influence of physical fitness level of youth basketball players on match-related physical performance
This study aimed to analyse the influence of different physical fitness levels of youth basketball players on match-related physical performance, using Random Forest clustering to distinguish between high-fitness level players and low-fitness level players. Twenty male youth basketball players completed the following physical performance tests in two separate sessions: bilateral and unilateral countermovement jumps, bilateral and unilateral horizontal jumps, single leg lateral jumps, the 20 m linear straight sprint test, the 505 test and a repeated sprint ability test. 1 week after the second testing day, players completed a simulated match while external loads were monitored using an ultra-wide band-based Local Positioning System. A Random Forest clustering was used to create two different clusters composed of players with similar physical fitness attributes (high- and low-fitness level players). Results indicate that the Random Forest clustering adequately discriminated among the players in different groups according to their physical fitness attributes. High-fitness level players covered more distance per min in all intensity thresholds and reached higher maximal speed and acceleration intensity during the simulated matches ( p < 0.05). These results may assist basketball practitioners in understanding running performance variations during matches and can be used to optimise preparation for individual players.
期刊介绍:
The Journal of Sports Engineering and Technology covers the development of novel sports apparel, footwear, and equipment; and the materials, instrumentation, and processes that make advances in sports possible.