## 4 Discussion  
The aim of this research study was to determine the correlation (predictive validity) of the UHCJ20m test in regards to the 100 m sprint (M100m), start acceleration (MRLS20m), and maximum running speed (MRFS20m). In addition, this research study aimed to determine the correlation between the kinematic parameters of the UHCJ20m motor test and performance in the 100 m sprint, start acceleration, and maximum running speed. The results of this study showed a moderate correlation in all tests (MRLS20m (R = 0.49), MRFS20m (R = 0.53), and M100m (R = 0.38)) with UHCJ20m. The results of the regression analysis confirmed a moderate significant correlation between the UHCJ20m test and the results in the 100 m sprint (Table 2). Explosive strength was found to have a dominant influence on sprint running results [11; 13; 14; 15; 16; 21; 22; 30; 31; 32; 33; 34]. For this reason, in processes of selection, control, and monitoring of sprinters, tests measuring explosive strength in the form of jumps and throws were used. The results of this research study were somewhat surprising, considering that many authors have found a much greater correlation between results in vertical and horizontal jumps and speed in 100 m sprints [11]. In simple terms, sprinting essentially consists of multiple single-legged or unilateral jumps that require an optimal relationship between stride length and frequency to achieve the greatest possible sprinting speed. Because running, especially sprinting, requires the use of both horizontal and vertical forces [8; 35], [PERSON] et al. [8] found that the results of tests involving horizontal jumps were better predictors of sprinting ability than vertical jumps. Sprint performance, like jump performance, depends on muscle contractility, the type and number of muscle fibers, inter/intramuscular coordination, and neural adaptation and activity [36; 37; 38]. For these reasons, a better prediction of 100 m sprint results based on the results of the UHCJ20m test was expected, as previously found by [PERSON] [27] for the 60 m sprint in girls. It was previously found that the correlation between unilateral horizontal jumps and sprint time and speed was greater in sprinters than in high school students [11]. It is likely that the same trend is present in the population of other sports that do not specialize in the 100 m sprint running. When analyzing the results of this study on the performance of the subjects in the M100m sprint test (Table 1) and comparing them with the results of elite sprinters [39], it is obvious that the results of the subjects were much worse, but when comparing these results with the results of other populations of athletes and the population of sports students, they were similar or slightly better [32; 38]. Some authors have provided the following values of sports students: 12.20 \(\pm\) 0.36 s [11], 13.00 \(\pm\) 0.51 s [38], and 12.60 \(\pm\) 0.9 s [39]. Elite sprinters who competed in the finals of the Olympic Games in 2008 and 2012 had average results of 9.96 \(\pm\) 0.05 s and 9.86 \(\pm\) 0.10 s, respectively, not including the results of [PERSON], who ran in these finals [40]. Slovenian elite sprinters run the 100 m on average in 10.52 \(\pm\) 0.19 s. The difference in the results of the 100 m sprint between elite sprinters and students is 3 s and 2.5 s, respectively, suggesting a specific training process, a learning process, genetically inherited abilities, and several years of selection of sprinters. [PERSON] [32] investigated the 100 m sprint in student athletes and obtained similar results, which averaged 13.00 \(\pm\) 0.51 s, confirming similar speed/sprint abilities of the subjects and also a greater variability of results within the group of subjects. The 100 m sprint run can be divided into start, start acceleration, maximum running speed, and deceleration, considering structural features [32; 41; 42; 43]. The results of the