Relationship between External and Internal Workloads in Soccer Players

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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CreationDate 2019-09-09
Creator Rossi, Alessio, alessio.rossi2@gmail.com, orcid.org/0000-0002-6400-5914
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Owner Rossi, Alessio, alessio.rossi2@gmail.com, orcid.org/0000-0002-6400-5914
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ThematicCluster Text and Social Media Mining
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Author Rossi Alessio
Maintainer Rossi Alessio
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Last Updated 28 April 2020, 00:01 (CEST)
Created 28 April 2020, 00:01 (CEST)