Precision Training Via Causal Machine Learning: Modeling Rating of Perceived Exertion in Professional Soccer Players

This study aimed to explore the use of predictive and prescriptive machine-learning models for managing training loads in professional soccer, with a focus on the rating of perceived exertion (RPE). Using data from a Belgian Pro League club, we evaluated the effectiveness of these models in predicting and prescribing optimal training regimens.

Data from 14 players across a full competitive season were analyzed. Predictive models including linear regression, random forest, and XGBoost were compared using the root-mean-square error and the mean absolute error. SHapley Additive exPlanations values were used to interpret feature importance. A prescriptive model based on the counterfactual recurrent network was developed to optimize training inputs for desired outcomes.

The XGBoost model demonstrated the best predictive performance (root-mean-square error: 1.262), with session distance identified as the most significant driver of RPE. While the prescriptive counterfactual recurrent network model showed slightly lower predictive accuracy (root-mean-square error: 1.379), its unique advantage lies in estimating counterfactual outcomes, allowing for the simulation of future RPE trajectories under different potential training plans and providing actionable insights for personalized training prescription.

Predictive modeling effectively estimates RPE, and prescriptive modeling offers the added benefit of optimizing training strategies. The integration of these approaches supports data-driven decisions in professional soccer, enhancing player performance and recovery. Future research should expand sample sizes and validate these methods across diverse sports and contexts.