Four picked matchmaking having race Pmaximum are represented towards Shape dos

Four picked matchmaking having race Pmaximum are represented towards Shape dos

During sprinting FV matchmaking i found: (a) weak so you’re able to average correlations that have strategy diving, (b) weakened to help you reasonable bad correlations with CoD performance, but off correlations having correct foot from inside the 505 try, and you will (c) negative weakened in order to very strong correlations which have sprinting feature towards the some other distances

Modified T-test was in moderate to high negative correlation (p < 0.01) with F0, Pmax and RFmax in sprint (r = ?0.42 to ?0.58). The negative correlation means that the subjects with faster T-test times exhibited higher values of FV outcome variables. In linear regression model, 34 sito single incontri over 60.7% of the variance in modified T-test times was explained with sprint Pmax, with no additional contribution of other variables.

The performance of 505 test on the left leg was in moderate negative correlation (p = 0.011–0.032) with RFmax (r = ?0.32) and sprint Pmax (r = ?0.38). The direction of these correlations implied that 505 test performance on the left leg improved with higher values of reported FV outcome variables. In linear regression model, 13.7% of the variance in 505 time on the left leg was explained with sprint Pmax, with no additional contribution of other variables. 505 test performance on the right leg was not correlated with any of the FV variables (p = 0.059–0.724). CoD deficit on the left leg (p = 0.045–0.021) was in moderate correlation with sprint F0 (r = 0.35), slope of the FV relationship in sprint (r = ?0.30) and RFmax (r = ?0.32). CoD deficit on the right leg was in moderate to high correlation with F0 (r = 0.54), Pmax (r = 0.38), slope of the FV relationship (r = ?0.45), RFmax (r = 0.48), and DRF (r = ?0.44). Sprint F0 explained 11.6% and 34.1% of the variance CoD deficit on left and right legs, respectively. There were no significant correlations between the outcome variables of FV variables in vertical jump and CoD performance or CoD deficit.

The 5 and 10 m sprint time was in moderate to very strong correlation (r = 0.43–0.93; p < 0.05) with F0 (negative correlation), Pmax (negative correlation), slope of the FV relationship, RFmax (negative correlation), and DRF. In linear regression model, RFmax alone explained 85.6 and 78.2% of the variance in 5 and 10 m sprint times, respectively, with additional contribution of F0 for 5 m time (88.2% of the variance explained). The 15 and 25 m sprint times were in moderate to very strong negative correlation (r = ?0.33 to ?0.93; p < 0.05) with RFmax, Vopt and maximal speed. Pmax alone explained 71.9% of the variance in 15 m sprint and 87.7% of the variance in the 25 m sprint, with no contribution from other variables.

Discussion

The purpose of this study was to examine the association between the FV relationship variables obtained in linear sprint and vertical jumps, and volleyball-specific approach jump performance, linear sprint and CoD ability (modified T-test, 505 test and CoD deficit) on a sample of young male volleyball players. Our results show that the only correlation between FV relationship in vertical jump and performance variables was between F0 and approach jump height. In regression models, sprinting Pmax was included as a predictor of approach jump performance and T-test performance, while RFmax was a predictor of sprinting ability on all distances. Therefore, approach jump performance seems to be influenced both by sprint and jumping FV profiles, while FV sprint variables alone contributed to explaining linear sprinting and CoD ability.

To our knowledge, only one study to date have examined the association between FV relationship and specific sport performance of volleyball players (Baena-Raya et al., 2021). The main finding of their study was that the F0 obtained from vertical jumping and the V0 obtained from sprinting, were strongly associated with both spike and serve ball speeds. These mechanical variables were able to explain approximately 20–36% of the variability in spike and serve speeds. Along with results in our study, those observations could help coaches implement specific FV profile-based training programs to improve specific mechanical capabilities that determine specific athletic performance, such as approach jump height, linear acceleration, and CoD ability, as well as to improve specific volleyball performance such as spike and serve ball speed in male volleyball players. The specific finding of our study is that performance proxies (approach jump, CoD tests, and sprinting) were related mostly to sprint-based FVP variables. It seems that producing high horizontal power is one of the paramount abilities underpinning sports performance. It has already been shown that maximal horizontal power is key determinant of linear sprinting performance (Morin et al., 2012). This study highlights that sprint Pmax is also related to superior CoD performance and jumping ability. On the other hand, approach jump performance appears to be related to vertical force and horizontal power production capacity.