## 1 Introduction  
Monitoring training load is a fundamental process to maximize the physical capacity of athletes and to manage their fatigue throughout the season [1]. An athlete's training load can be quantified by external (e.g., global position system and video analysis) and internal parameters (e.g., rate of perceived exertion, heart rate, and lactate). The external training load represents the dose performed, while the internal training load reflects the psycho-physiological response of the athlete [2; 3; 4; 5].  
The game-based nature of team sports can generate inter-individual variation in external training load, resulting in different internal training loads [1; 6]. In soccer the number of matches played during the season together with the inter-individual variation related to physical levels, role positions [7; 8], and technical and tactical requirements can lead to a training imbalance, leaving some athletes at risk of overtraining and others failing to reach an adequate training stimulus that could potentially enhance the risk of injuries [9; 10]. Therefore, the implementation of monitoring models able to understand which specific training doses should be applied to individual athletes, and which markers of external load influence the athletes' internal load should be studied [11; 12]. Indeed, several studies show that the relationship between planned and perceived training load is weak: the training sessions designed to be easy or intermediate are perceived as harder by players, while the sessions designed to be hard are perceived as less intense [13; 14].  
Recently, the relationship between internal- and external-load parameters has been investigated to demonstrate that both rate of perceived exertion (RPE) and training load (S-RPE) are significantly related to several indicators of external physical load (i.e., high-speed running distance (>14.4 km/h), number of impacts, and number of accelerations (>3 m\(\cdot\)s\({}^{-2}\)) [12]. Furthermore, the magnitude of the within-individual correlations significantly reduces when adjusted for the effects of the other variables, and this may reflect the fact that many additional factors may contribute to the perception of intensity in intermittent team-sport exercise [6]. Similarly, other studies provide new evidence to support the use of global position system (GPS) data as a valid global indicator of training responses and intensity in elite rugby, Australian football, and in sub-elite soccer [6; 15; 16]. For this reason, the use of new advanced sensing systems that allow predicting RPE and S-RPE based on external load could be a useful tool to improve training periodization. Previous studies demonstrated that measures which derivate from RPE and S-RPE could be useful tools for monitoring training loads. Hence, assessing external workloads becomes crucial to optimize athlete-monitoring practices [17; 18].  
The main aim of this study is to provide a machine learning (ML) model that is able to predict the players' internal load by external ones extracted from GPS raw data. This ML model also allows athletic trainers and coaches to detect the discrepancy between the effort that the player should have perceived for a specific external load and the effort that the player has perceived, hence highlighting possible problems induced by training periodization. Based on the cited papers, we hypothesized that it is possible to predict internal load by the external one accurately and that we can further improve the prediction by taking into consideration contextual features such as physiological aspects and individual characteristics. Moreover, the main goal of this paper is to improve the understanding of the discrepancy between the internal load recorded and the one predicted, so to help athletic trainers and coaches better periodize training, reducing the stress of the players while maximizing the effect of training.