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Modeling the prediction of the session rating of perceived exertion in soccer : unraveling the puzzle of predictive indicators

Youri Geurkink (UGent) , Gilles Vandewiele (UGent) , Maarten Lievens (UGent) , Filip De Turck (UGent) , Femke Ongenae (UGent) , Stijn Matthys (UGent) , Jan Boone (UGent) and Jan Bourgois (UGent)
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Abstract
PURPOSE: To predict the session rating of perceived exertion (sRPE) in soccer and determine its main predictive indicators. METHODS: A total of 70 external-load indicators (ELIs), internal-load indicators, individual characteristics, and supplementary variables were used to build a predictive model. RESULTS: The analysis using gradient-boosting machines showed a mean absolute error of 0.67 (0.09) arbitrary units (AU) and a root-mean-square error of 0.93 (0.16) AU. ELIs were found to be the strongest predictors of the sRPE, accounting for 61.5% of the total normalized importance (NI), with total distance as the strongest predictor. The included internal-load indicators and individual characteristics accounted only for 1.0% and 4.5%, respectively, of the total NI. Predictive accuracy improved when including supplementary variables such as group-based sRPE predictions (10.5% of NI), individual deviation variables (5.8% of NI), and individual player markers (17.0% of NI). CONCLUSIONS: The results showed that the sRPE can be predicted quite accurately using only a relatively limited number of training observations. ELIs are the strongest predictors of the sRPE. However, it is useful to include a broad range of variables other than ELIs, because the accumulated importance of these variables accounts for a reasonable component of the total NI. Applications resulting from predictive modeling of the sRPE can help coaching staff plan, monitor, and evaluate both the external and internal training load.
Keywords
Orthopedics and Sports Medicine, machine learning, sRPE, soccer, team sports, training load

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Please use this url to cite or link to this publication:

Chicago
Geurkink, Youri, Gilles Vandewiele, Maarten Lievens, Filip De Turck, Femke Ongenae, Stijn Matthys, Jan Boone, and Jan Bourgois. 2019. “Modeling the Prediction of the Session Rating of Perceived Exertion in Soccer : Unraveling the Puzzle of Predictive Indicators.” International Journal of Sports Physiology and Performance.
APA
Geurkink, Y., Vandewiele, G., Lievens, M., De Turck, F., Ongenae, F., Matthys, S., Boone, J., et al. (2019). Modeling the prediction of the session rating of perceived exertion in soccer : unraveling the puzzle of predictive indicators. INTERNATIONAL JOURNAL OF SPORTS PHYSIOLOGY AND PERFORMANCE.
Vancouver
1.
Geurkink Y, Vandewiele G, Lievens M, De Turck F, Ongenae F, Matthys S, et al. Modeling the prediction of the session rating of perceived exertion in soccer : unraveling the puzzle of predictive indicators. INTERNATIONAL JOURNAL OF SPORTS PHYSIOLOGY AND PERFORMANCE. 2019;
MLA
Geurkink, Youri et al. “Modeling the Prediction of the Session Rating of Perceived Exertion in Soccer : Unraveling the Puzzle of Predictive Indicators.” INTERNATIONAL JOURNAL OF SPORTS PHYSIOLOGY AND PERFORMANCE (2019): n. pag. Print.
@article{8599704,
  abstract     = {PURPOSE: To predict the session rating of perceived exertion (sRPE) in soccer and determine its main predictive indicators.
METHODS: A total of 70 external-load indicators (ELIs), internal-load indicators, individual characteristics, and supplementary variables were used to build a predictive model.
RESULTS: The analysis using gradient-boosting machines showed a mean absolute error of 0.67 (0.09) arbitrary units (AU) and a root-mean-square error of 0.93 (0.16) AU. ELIs were found to be the strongest predictors of the sRPE, accounting for 61.5\% of the total normalized importance (NI), with total distance as the strongest predictor. The included internal-load indicators and individual characteristics accounted only for 1.0\% and 4.5\%, respectively, of the total NI. Predictive accuracy improved when including supplementary variables such as group-based sRPE predictions (10.5\% of NI), individual deviation variables (5.8\% of NI), and individual player markers (17.0\% of NI).
CONCLUSIONS: The results showed that the sRPE can be predicted quite accurately using only a relatively limited number of training observations. ELIs are the strongest predictors of the sRPE. However, it is useful to include a broad range of variables other than ELIs, because the accumulated importance of these variables accounts for a reasonable component of the total NI. Applications resulting from predictive modeling of the sRPE can help coaching staff plan, monitor, and evaluate both the external and internal training load.},
  author       = {Geurkink, Youri and Vandewiele, Gilles and Lievens, Maarten and De Turck, Filip and Ongenae, Femke and Matthys, Stijn and Boone, Jan and Bourgois, Jan},
  issn         = {1555-0265},
  journal      = {INTERNATIONAL JOURNAL OF SPORTS PHYSIOLOGY AND PERFORMANCE},
  language     = {eng},
  title        = {Modeling the prediction of the session rating of perceived exertion in soccer : unraveling the puzzle of predictive indicators},
  url          = {http://dx.doi.org/10.1123/ijspp.2018-0698},
  year         = {2019},
}

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