Relationship between External and Internal Workloads in Elite Soccer Players: Comparison between Rate of Perceived Exertion and Training Load

被引:24
|
作者
Rossi, Alessio [1 ]
Perri, Enrico [2 ]
Pappalardo, Luca [3 ]
Cintia, Paolo [1 ]
Iaia, F. Marcello [2 ]
机构
[1] Univ Pisa, Dept Comp Sci, I-26127 Pisa, Italy
[2] Univ Milan, Dept Biomed Sci Hlth, I-20122 Milan, Italy
[3] CNR, Ist Sci & Tecnol Informaz A Faedo, I-56127 Pisa, Italy
来源
APPLIED SCIENCES-BASEL | 2019年 / 9卷 / 23期
关键词
sports analytics; external workload; training volume; internal workload; sports data science; SESSION-RPE METHOD; VALIDITY; RELIABILITY; PERCEPTION; INJURY;
D O I
10.3390/app9235174
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
The use of machine learning (ML) in soccer allows for the management of a large amount of data deriving from the monitoring of sessions and matches. Although the rate of perceived exertion (RPE), training load (S-RPE), and global position system (GPS) are standard methodologies used in team sports to assess the internal and external workload; how the external workload affects RPE and S-RPE remains still unclear. This study explores the relationship between both RPE and S-RPE and the training workload through ML. Data were recorded from 22 elite soccer players, in 160 training sessions and 35 matches during the 2015/2016 season, by using GPS tracking technology. A feature selection process was applied to understand which workload features influence RPE and S-RPE the most. Our results show that the training workloads performed in the previous week have a strong effect on perceived exertion and training load. On the other hand, the analysis of our predictions shows higher accuracy for medium RPE and S-RPE values compared with the extremes. These results provide further evidence of the usefulness of ML as a support to athletic trainers and coaches in understanding the relationship between training load and individual-response in team sports.
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页数:11
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