## 4 Discussion
These findings indicate that individual measurement error is considerably higher than previously measured training-induced changes in 10, 20, and 40 m split times observed in elite youth soccer players (i.e., 0.04, 0.2, and 0.3 s, respectively) [26; 27]. Consequently, the practical implications of GPEXE It for assessing split times in training and practice are limited, given the magnitude of individual measurement error relative to the expected changes in split times due to speed performance enhancement. This suggests a need for consideration of the device's limitations when utilizing it for the assessment of linear or curvilinear sprint timing in practical settings. For measurement of CS and LS performance, the MSS seems to be more appropriate variable when utilizing the GPEXE It. The high ICC between the criterion and the systematic underestimation of the MSS align with findings from previous studies that reported significant bias when using GPS devices for linear sprint MSS assessments [9; 14; 28]. However, the Bland-Altman graphs between GPS and TG during LS and CS revealed lower agreement at an individual level. Among all sprints, the 95% LoA intervals for CS to the right and LS were the smallest, ranging from 0.09 to \(-\)0.44 and 0.19 to \(-\)0.35 m\(\cdot\)s\({}^{-1}\), respectively. Previous studies that investigated the effects of sprint training on MSS found changes lower than 0.5 m\(\cdot\)s\({}^{-1}\) to be significant [29]. However, when MSS during LS and CS is measured, the GPEXE LT might not be accurate enough to detect such changes. Running fast over linear and curvilinear distances is particularly important in team-based sports such as football or rugby [2]. Because the sports are performed outdoors, GPS systems could be time-efficient tool for the assessment of athlete's speed performance in CS and LS. Overall, our study confirmed that the GPEXE It is viable for the assessment of LS and CS test performances. We advise the use of MSS rather than the split times when assessing sprint performance with the two tests. However, the aforementioned limitationsshould be considered, especially in highly trained athletes where very small improvements in MSS are considered to be important [5]. This study had some limitations that should be acknowledged. First, the theoretical MSS of TG was used as a criterion. It is essential to note that the primary focus of this study was to evaluate sprint performance in CS, where TG represents the most common method for determining performance [4]. Additionally, the use of single-beam TG introduces the potential for measurement errors in split times [30]. To mitigate this, we rigorously standardized the protocol, visually anticipating TG initiation during sprint start by an experienced researcher, and used corrected split times and MSS [19]. Another consideration is the population sample, which comprised trained student athletes from various sport disciplines. Since the GPEXE It usability and relevance to CS and LS performance primarily pertain to team sports like soccer, we acknowledge the potential benefit of repeating the study on elite soccer players. This approach would allow for the derivation of specific MDC values. At the same time, it is worth noting that our sample did include 12 amateur soccer players. Finally, we used automatically filtered GPEXE It data, while previous studies have suggested that specific filters provide the most reliable results [11]. Our decision to use automatically filtered data were driven by the goal of aligning the results with the practices of practitioners who typically utilize preprocessed data directly from the GPEXE application. This pragmatic approach aimed to enhance the practical relevance and applicability of the study findings in real-world settings.