## 4 Discussion
### ICC and SEM Should Always Be Reported Together  
The ICC is probably the most popular reliability metric in the literature. Our analysis showed that the ICC was often included as a reliability metric but with large variability, ranging from poor = 0.004 (i.e., strain from session-RPE) [131] to excellent = 0.98 (i.e., contraction time measured with tensionyography or jump height of a CM) [57; 131]. Particularly, in the case of tensionyography, although there is sufficient data in favor of its good-to-excellent relative reliability in the scientific literature (i.e., ICC = 0.70-0.99) [140] in agreement with our findings (i.e., ICC = 0.80-0.98), more evidence is necessary for identifying the accuracy of this tool, according to a current systematic review with meta-analysis [141], mainly because reliability based only on the ICC cannot be recommended [142]. This is because there is a relationship between the ICC and between-subjects variability, where the heterogeneity of the subjects is a determinant for the ICC obtained [25]. An excellent ICC can mask poor trial-to-trial consistency when between-subjects variability is high. Conversely, a poor ICC can be found even when trial-to-trial variability is low when the between-subjects variability is low. In this case, the homogeneity of the subjects' means will make it difficult to differentiate between subjects even though the absolute measurement error is small. In other words, if individuals differ little from each other, the ICC may be poor, even if the trial-to-trial variability is small [25]. Thus, an examination of the ICC in conjunction with the SEM is, therefore, recommended [142]. For example, anaverage HR, with ICC = 0.77 (good) and SEM = 3.0% (good), in one case [127] would be preferable than the same parameter with ICC = 0.89 (good) and SEM = 27% (poor) [124]. On the other hand, the SEM is not affected by between-subjects variability, as occurs with the ICC. The SEM has been used to define the boundaries in which a subject's true score lies, and it can be calculated as both absolute and relative scores [25; 26; 37; 38]. However, the range of variation of this veracity metric for the found parameters was high (i.e., between good = 0.3% for total distance covered, as measured with EPTS, and poor = 89.7% for accelerations >4 m/s\({}^{2}\), measured with EPTS) [64; 73]. This would suggest that for the same tool, some parameters could be more recommended than others. In this regard, some researchers are using a limit of 10% of the SEM to determine which parameters can be used in subsequent analyses [35; 87; 143]. Although its use is strongly recommended by the specialized literature [25; 26], some points of confusion can be highlighted. The first is that it is called either standard error of measurement (SEM), typical error of measurement (TEM), or typical error (TE), even with the same form of calculation. The second point, which is most impactful, is that the SEM is commonly referred to as the coefficient of variation (CV) and reported as a percentage, but the calculation is not performed by dividing the standard deviation by the mean and subsequently multiplied by 100 [144]. These facts can easily drive researchers and practitioners to misinterpretations. Furthermore, the SEM can also be used in the interpretation of individual scores as the minimum difference (MD) or minimum detectable change (MDC) [25; 26]. In the present study, the MDC ranged from 48.0% for the distance at maximal sprint speed recorded with EPTS to 305.0% for the very high-speed running time obtained with EPTS [76] with regards to the group average. This extremely high variability could hinder the verification of real changes in an individual's performance. Of note, the MDC in professional soccer players is not easily found in scientific literature. Apart from the included study in the current systematic review, another study reported an MDC between 1.0% and 30.0% for performance in a new agility test [145].