Objectives: To determine the impact of between limb asymmetries in hop performance on change of direction speed (CODS). Design and Methods: Twenty-two multisport collegiate athletes (mean ± SD; age: 21.8 ± 3.4 years, height: 178.1 ± 6.7 cm, mass: 73.5 ± 7.1kg) performed three single and triple horizontal hops for distance per limb, followed by three modified 505 and 90˚cut CODS trials each side to establish imbalances between right and left, and dominant (D) and non-dominant (ND) limbs. Limb dominance was defined as the limb that produced the furthest hop or faster CODS performance. Results: Paired sample t-tests revealed no significant differences in hop performance and CODS performance between right and left limbs (p > 0.05, g ≤ 0.11), however, significant differences were observed when comparing D and ND limbs (p < 0.001, g = 0.46-0.61). No significant correlations were observed between hop imbalance and CODS performance (p > 0.05, r ≤ 0.35). Low agreements (32-55%) were demonstrated between like for like identifications of asymmetry for CODS and hop performance. Conclusions: Imbalances in hop and CODS were present; however, greater hop imbalances were not detrimental to CODS. Furthermore, the D limb for hopping did not necessarily correspond to faster performance from that limb during 180˚ turns and 90˚ cuts (plant foot). Collegiate male multi-sport athletes with imbalances within the range reported within this study (≤ 15%) should not experience associated CODS detriments.
Objectives: The purpose of this study was to examine the relationship among isometric mid-thigh pull (IMTP) variables, jump variables and sprint times in collegiate soccer players. Additionally, this study was conducted to demonstrate that strength characteristics influence the relationship between jump variables and sprint times. Design and Methods: Twenty-five collegiate soccer players performed IMTP, jump and sprint assessments. For IMTP, the force output at 100ms (F100ms) and peak force (PF) were analyzed. Countermovement jump (CMJ) and drop jump (DJ) index were measured. A 30m sprint was performed, and the times at 10m, 20m and 30m were recorded. Pearson’s product-moment correlation and a one-way analysis of variance (ANOVA) were used at p = 0.05. A cluster analysis was performed to divide all the subjects. Results: The F100ms significantly correlated with DJ-index (r = 0.433) and sprint times at 20-30m (r = -0.444). All the subjects were separated into high (HG: N = 9), medium (MG: N = 7) and low (LG: N = 9) groups based on the F100ms, because the coefficient of variation for F100ms was high (34.3%). There was a strong significant relationship between CMJ and sprint time at 10-20m in HG (r = -0.915), however there were no significant relationship in MG and LG. Conclusions: The F100ms can be used as an indicator for identifying athletes who have a statistically significant relationship between CMJ and flying sprint times. The results of the present study suggested that coaches should realize F100ms might pro vide the foundation to improve the sprint performance.
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