Predicting ratings of perceived exertion in youth soccer using decision tree models

Jakub Marynowicz; Mateusz Lango; Damian Horna; Karol Kikut; Marcin Andrzejewski

doi:10.5114/biolsport.2022.103723

Predicting ratings of perceived exertion in youth soccer using decision tree models

Biol Sport. 2022 Mar;39(2):245-252. doi: 10.5114/biolsport.2022.103723. Epub 2021 Apr 9.

Authors

Jakub Marynowicz^{1

2}, Mateusz Lango³, Damian Horna³, Karol Kikut², Marcin Andrzejewski⁴

Affiliations

¹ Department of Theory and Methodology of Team Sport Games, Poznan University of Physical Education, Poznań, Poland.
² KKS Lech Poznań S.A. - Football Club, Poznań, Poland.
³ Institute of Computer Science, Poznan University of Technology, Poznań, Poland.
⁴ Department of Methodology of Recreation, Poznan University of Physical Education, Poznań, Poland.

Abstract

The purpose of this study was to determine the effectiveness of white-box decision tree models (DTM) for predicting the rating of perceived exertion (RPE). The second aim was to examine the relationship between RPE and external measures of intensity in youth soccer training at the group and individual level. Training load data from 18 youth soccer players were collected during an in-season competition period. A total of 804 training observations were undertaken, with a total of 43 ± 17 sessions per player (range 12-76). External measures of intensity were determined using a 10 Hz GPS and included total distance (TD, m/min), high-speed running distance (HSR, m/min), PlayerLoad (PL, n/min), impacts (n/min), distance in acceleration/deceleration (TD ACC/TD DEC, m/min) and the number of accelerations/decelerations (ACC/DEC, n/min). Data were analysed with decision tree models. Global and individualized models were constructed. Aggregated importance revealed HSR as the strongest predictor of RPE with relative importance of 0.61. HSR was the most important factor in predicting RPE for half of the players. The prediction error (root mean square error [RMSE] 0.755 ± 0.014) for the individualized models was lower compared to the population model (RMSE 1.621 ± 0.001). The findings demonstrate that individual models should be used for the assessment of players' response to external load. Furthermore, the study demonstrates that DTM provide straightforward interpretation, with the possibility of visualization. This method can be used to prescribe daily training loads on the basis of predicted, desired player responses (exertion).

Keywords: Fatigue; GPS; RPE; Team sport; Training load; Training monitoring.