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Open AccessArticle

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

1
Department of Computer Science, University of Pisa, 26127 Pisa, Italy
2
Department of Biomedical Science for Health, University of Milan, 20122 Milan, Italy
3
Istituto di Scienza e Tecnologie dell’Informazione “A. Faedo” of the National Research Council, 56127 Pisa, Italy
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(23), 5174; https://doi.org/10.3390/app9235174
Received: 29 October 2019 / Revised: 21 November 2019 / Accepted: 26 November 2019 / Published: 28 November 2019
(This article belongs to the Special Issue Computational Intelligence and Data Mining in Sports)
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. View Full-Text
Keywords: sports analytics; external workload; training volume; internal workload; sports data science sports analytics; external workload; training volume; internal workload; sports data science
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MDPI and ACS Style

Rossi, A.; Perri, E.; Pappalardo, L.; Cintia, P.; Iaia, F.M. Relationship between External and Internal Workloads in Elite Soccer Players: Comparison between Rate of Perceived Exertion and Training Load. Appl. Sci. 2019, 9, 5174. https://doi.org/10.3390/app9235174

AMA Style

Rossi A, Perri E, Pappalardo L, Cintia P, Iaia FM. Relationship between External and Internal Workloads in Elite Soccer Players: Comparison between Rate of Perceived Exertion and Training Load. Applied Sciences. 2019; 9(23):5174. https://doi.org/10.3390/app9235174

Chicago/Turabian Style

Rossi, Alessio; Perri, Enrico; Pappalardo, Luca; Cintia, Paolo; Iaia, F. M. 2019. "Relationship between External and Internal Workloads in Elite Soccer Players: Comparison between Rate of Perceived Exertion and Training Load" Appl. Sci. 9, no. 23: 5174. https://doi.org/10.3390/app9235174

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