This study investigates how the evaluation of special endurance manifests itself in players' perceptions of their physiological readiness in the context of elite football training environments. The rise of AI-based diagnostics has enabled new forms of performance tracking, but the precision of these systems, particularly the variation in shaping individualized feedback, is not well understood. This research aims at examining the task of endurance profiling based on data deriving from wearable sensors, for instance inertial measurement units and other GPS-based systems, with the development of a relevant model for neuromuscular fatigue assessment. We employ the machine learning regression method to analyze time-series datasets conducted with academy-level players, and we identify six key mechanisms of endurance adaptation, namely energy system balance, motor unit recruitment, acceleration consistency, biomechanical efficiency, cardiovascular load, and recovery rate. Our results illustrate that from a physiological monitoring perspective, individualized feedback in training load adjustment is a key positive element of performance planning. The study furthers understanding of the implications from sensor-based metrics and AI analytics on training personalization. In this paper, a methodological and instrumental solution to the current problem of creating the most effective diagnostic framework in a football-specific endurance context is proposed.
Shermuxamedov et al. (Fri,) studied this question.