## 4 Discussion
This study shows that, based on 25 thrown pitches, an individual load distribution can be built with the within-individual load variability and the load magnitude (average), which can be used for injury assessment. Longitudinal studies have shown that fatigue influences injury risk [23; 24]. Thus, it is too simplistic to assume that the load magnitude, load variability, and injury threshold are fixed parameters over time and are not influenced by, for example, fatigue. It was found that the development of fatigue over time increases the external valgus torque magnitude [25], indicating a shift of the load distribution toward the injury threshold. However, it is unknown whether fatigue also influences the within-individual load variability on a micro-level (within a session) or macro-level (between sessions). Executing a study that investigates fatigue in relation to load variability and the effects on an estimated injury threshold based on our model could provide us with new insights. In terms of practical implications, the presence of within-individual load variability raises the question of whether this load variability is also related to performance. We, therefore, performed a post hoc analysis and analyzed how the pitchers with load variability were distributed in a graph displaying the relationship between ball speed and external valgus torque (Figure 3). Each dot in Figure 3 represents a single pitch and each  
Figure 2: Schematic overview of within-individual elbow load variability (width of the normal distributions) and injury thresholds (red vertical lines). All four quadrants have the same load magnitude (average), shown as the vertical dashed lines. The horizontal axis of the quadrant shows the load variability, and the vertical axis shows the load capacity. The state on the left top panel shows the lowest chance, the right top and left bottom have the same chance, and the right bottom has the highest chance of sustaining an injury. Be aware that a shift in the load magnitude is also possible, but not represented here. color represents a pitcher, corresponding with the colors in Figure 1. On a group level, a weak relationship between external valgus torque and ball speed is present. When individual characteristics are included, this results in a much stronger relationship, which is in agreement with the study of [PERSON] et al. (2019) [9]. The heights of the ellipses, indicating the within-individual load variability, are randomly scattered in this graph. Linear regression showed that, unlike the valgus torque magnitude, the within-individual valgus torque variability is unrelated to the pitcher average (\(p=0.309\), \(R^{2}=0.1146\)) or maximal (\(p=0.195\), \(R^{2}=0.1791\)) ball speed. Thus, based on these preliminary results, it seems that within-individual elbow load variability is not related to performance in this group of pitchers. In practical terms, this means that a pitcher should strive to minimize their elbow load variability as it might reduce the chance of sustaining an injury without influencing performance. It is known that a \"proper\" technique is related to the elbow valgus torque [7], and thus it might also be related to the load variability. For injury prevention, it should be investigated whether a pitcher is able to minimize their elbow load variability consciously. The study population was a homogenous group of youth elite baseball pitchers (age 15-22). It is known that the magnitude of external elbow valgus torque increases with the level of play [11]. The group average valgus torque variability did not show significant differences between levels of play according to [PERSON] et al. (2009) [26], indicating that our results showing differences between pitchers in within-individual load variability can be extended to other levels of play. A large difference between our study and the study of [PERSON] et al. (2009) is that they only included the 5 fastest pitches in their analysis to  
Figure 3: The relationship between ball speed and valgus torque.