Modeling the Prediction of the Session Rating of Perceived Exertion in Soccer: Unraveling the Puzzle of Predictive Indicators
| Autor: | Jan Bourgois, Stijn P.J. Matthys, Filip De Turck, Jan Boone, Gilles Vandewiele, Femke Ongenae, Maarten Lievens, Youri Geurkink |
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| Rok vydání: | 2019 |
| Předmět: |
Adult
Rating of perceived exertion Physical Exertion Applied psychology Physical Therapy Sports Therapy and Rehabilitation Workload 030229 sport sciences Models Theoretical Young Adult 03 medical and health sciences 0302 clinical medicine Soccer Humans Orthopedics and Sports Medicine 030212 general & internal medicine Session (computer science) Training load Psychology Physical Conditioning Human |
| Zdroj: | International Journal of Sports Physiology and Performance. 14:841-846 |
| ISSN: | 1555-0273 1555-0265 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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