Résumé
Professional football players face high physical demands throughout the season, which have been increasing steadily for many years.1 To ensure players’ health (i.e., optimize physical performance and reduce injury risk), practitioners have developed monitoring strategies based on external and/or internal indicators.2 Yet, there are several operational (i.e., schedule, staff turnover) and theoretical (i.e., non-linear relationship, multifactorial aspect of the activity) limitations hindering their daily use in elite football.3 To overcome these issues, recent sports science literature has shown interest in using machine learning models.4 Heart rate (HR), a surrogate measure of the cardiorespiratory system,5 has raised some interest to monitor training status.6 We propose to use an indicator based on the difference between predicted and measured HR during specific football drills to track player fitness (ΔHR)7,8 as well asindicators related to HR kinetics (i.e., HR acceleration and recovery)9 and their difference with their predicted value to have a complete overview of the player’s cardiovascular status. The postulate underlying the monitoring of these indicators is intuitive: any deviation from the expected normal behavior (i.e., prediction) informs the practitioner about the player’s training status (e.g., for ΔHR, a higher or lower physiological cost than expected for a given external load (i.e., improved or reduced fitness).