## 1 Introduction  
The quantification of training load is a tool that coaches use more and more, with the aim of improving sports performance, since it helps to control the progression of players [1]. This is why training monitoring has become one of the most studied research topics in sport in recent years [2]. This monitoring allows staff to obtain quantitative and qualitative information related to an athlete's performance, which allows better understanding of their responses to exercise and the restructuring of training plans [2, 3]. Previous research has established different variables that influence training load in team sports. Among them are the characteristics of the training system [4] and the individual characteristics of the players [5]. In relation to this, the player's experience, position and playing time and their influence on basketball load have been analyzed [6, 7, 8]. However, there are few studies in which individual characteristics have been related to the weekly training load [9]. It has been proven that different performance indicators influence the load in different sports, both in training [6; 10; 11] and in competition [12; 13; 14]. There is scientific evidence on which performance indicators are the most important to win a game ([15; 16; 17], and the effect of some situational variables on the game result have been verified. The most studied situational variables are: game location [18; 19; 20], quality of opposition [21; 22; 23], season phase [9; 24; 25], score-line [3; 19] and recovery cycle [9; 26]. Generally, situational variables have been related to performance indicators. The only situational variables that have also been related to weekly training load are game schedule, playing time and season phase [4; 27; 28]. The effect of situational factors on internal and external load have been studied recently, with results that lead to a deeper understanding of the training and competition processes. The place of play, phase of the season, result of the game, and score and quality of the opponent can explain 45% of the subjective internal load measured through RPE. In addition, it has been identified that female players in basketball have a higher RPE when playing away from home and playing against weaker opponents [29]. Recent studies have shown how loads are affected by variable situations. [PERSON], [PERSON], [PERSON] and [PERSON] [30] found that in semi-professional basketball players there were differences in the volume of movements, high intensity actions, impacts and jumps depending on the playing position. The performance of the players decreased as the playing period increased. In addition, in balanced games there was the highest individual technical performance (PIR), while in unbalanced games more high intensity impacts were observed. However, they did not find differences in both physical and technical performance in terms of high difference in points per quarter. The outcome of the competition also influences the load of the players. Players who lose a match present higher values in kinematic variables, while players who win matches have a higher session RPE. The location of the match also influences the load, with the players playing at home presenting higher values in load (PLayerLoad) and in kinematic, objective internal load (HR) and subjective internal load (RPE) variables. This same trend is seen in matches in which the games are more balanced, with a smaller difference in points on the scoreboard [19]. Along the same lines, [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] [31] identified that, when an extra time period is played, the internal (RPE, HR) and external (changes of direction, accelerations) load indicators increased with respect to regular matches. On the contrary, the load borne by the players (PlayerLoad) decreased as the quarters progressed.