Relationship between external and internal workloads in elite soccer players: Comparison between rate of perceived exertion and training load

Rossi A., Perri E., Pappalardo L., Cintia P., Iaia F. M.

external workload  Sports analytics  training volume  Instrumentation  General Materials Science  sports analytics  sports data science  General Engineering  Process Chemistry and Technology  Internal workload  Computer Science Applications  Fluid Flow and Transfer Processes  Sports data science  internal workload  Training volume  External workload 

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 SRPE 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 SRPE 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.

Source: Applied sciences 9 (2019). doi:10.3390/app9235174

Publisher: Molecular Diversity Preservation International, Basel

Metrics