## 4 Discussion
For instance, variables such as DSL and HSRr are direct indicators of the physical stress experienced by players, while metrics like sRPE and total distance provide insights into perceived exertion and overall workload [53; 54]. The inclusion of positional data further emphasizes the importance of contextual factors, as different playing positions have varying physical demands and recovery profiles. Positional data in football have traditionally captured physical and tactical factors separately, but recent research emphasizes integrating player tracking data and spatiotemporal parameters [3; 4]. This approach combines physical and tactical variables to provide a more comprehensive understanding of individual player performance [3]. The relatively low predictive performance across all models highlights potential challenges in modeling RPE using objective training load metrics. RPE is naturally subjective and is affected by mental, environmental, and physical factors that may not be completely reflected in the available dataset. Future research should consider integrating additional contextual variables, such as athlete fatigue levels, motivation, and external conditions, to enhance predictive accuracy. Such integrative analysis is becoming increasingly important as it offers a holistic view of a player's contributions on the field [55]. In addition, affordable and non-invasive techniques, such as heart rate monitors and perceived effort scales, are commonly employed [24; 25]. These tools are practical for monitoring psychophysiological fatigue and changes in performance during matches and training sessions, making them accessible and effective for regular use in sports settings [24]. Overall, the DT model's effectiveness suggests that simpler models with interpretable decision rules may provide more reliable insights into RPE prediction. Future work can explore feature engineering techniques or hybrid models to further refine predictive performance and improve the practical application of ML models in training load monitoring. From research-practice issues, understanding the impact of psychophysiological variables on physical performance and training load is crucial. The relatively low accuracy suggests that incorporating objective biomechanical parameters, such as joint mechanical demands, can enhance the predictive performance of the models. This aligns with recent studies that emphasize the value of combining subjective and objective indicators for more accurate recovery assessments [56; 57]. Self-perception of motor competence can significantly influence motor performance, with higher perceived competence correlating with greater confidence and motivation, thereby enhancing performance [21; 22]. Conversely, low self-perception can lead to decreased motivation and increased anxiety, negatively impacting performance [20]. From a health perspective, mental health issues and interpersonal relationships also significantly affect physical performance [24, 25]. Mental health problems, such as depression, anxiety, and stress, can impair concentration, reduce energy levels, and disrupt sleep patterns, all of which negatively impact physical performance. Interpersonal relationships, including interactions with coaches, teammates, and family members, can either provide essential support and motivation or contribute to psychological stress, further influencing an athlete's ability to perform and recover effectively [58, 59]. The most important variables identified by the ML model were DSL, HSRr, sRPE, and sRPE_norm. Interestingly, TQR was eliminated from the model, externalizing the multifactorial nature of recovery. However, the control of perceived exertion seems to be preponderantly associated with body impacts (i.e., DSL) and high-intensity movements (i.e., HSRr, HMLD, and ACC). This research shows that the chosen parameters obtained from wearable sensors are successful in forecasting the recovery conditions of youth football players.