Relationships Between the External and Internal Training Load in Professional Soccer: What Can We Learn From Machine Learning?

PURPOSE: Machine learning may contribute to understanding the relationship between the external load and internal load in professional soccer. Therefore, the relationship between external load indicators and the rating of perceived exertion (RPE) was examined using machine learning techniques on a group and individual level.

METHODS: Training data were collected from 38 professional soccer players over two seasons. The external load was measured using global positioning system technology and accelerometry. The internal load was obtained using the RPE. Predictive models were constructed using two machine learning techniques, artificial neural networks (ANNs) and least absolute shrinkage and selection operator (LASSO), and one naive baseline method. The predictions were based on a large set of external load indicators. Using each technique, one group model involving all players and one individual model for each player was constructed. These models' performance on predicting the reported RPE values for future training sessions was compared to the naive baseline's performance.

RESULTS: Both the ANN and LASSO models outperformed the baseline. Additionally, the LASSO model made more accurate predictions for the RPE than the ANN model. Furthermore, decelerations were identified as important external load indicators. Regardless of the applied machine learning technique, the group models resulted in equivalent or better predictions for the reported RPE values than the individual models.

CONCLUSIONS: Machine learning techniques may have added value in predicting the RPE for future sessions to optimize training design and evaluation. Additionally, these techniques may be used in conjunction with expert knowledge to select key external load indicators for load monitoring.