Monitoring of cardiac adaptation in elite soccer players over a season through machine learning

Abstract

The aim of this study was to assess the evolution of professional soccer players’ training status by monitoring an indicator of cardiovascular fitness (ΔHR) over an entire season. The locomotor activity (GPS) and heart rate (HR) of 31 professional soccer players were recorded during small-sided games (SSG) during the 2022–2023 season. Individual predictive models of HR responses built using machine learning methods (i.e. Linear Regression, Support Vector Machine, Random Forest, and eXtreme Gradient Boosting) were trained on a dataset that contains GPS and weather data, Borg CR-10 scale scores and cumulative load. ΔHR was defined as the difference between predicted and measured HR responses. Robustness of models was assessed through a resampling procedure (n = 20). A difference in ΔHR between months was found (p