Kinetic and Kinematic Analysis for Assessing the Differences in Countermovement Jump Performance in Rugby Players

Abstract

The aim of this study was to ascertain the differences in kinetic and kinematic profiles between better and poorer performers of the vertical jump within a homogeneous group of trained adults. Fifty rugby players were divided into low scoring (LOW) and high scoring (HIGH) groups based on their performance in the vertical jump. The force, velocity, displacement, and RFD-time curves were analyzed in order to determine the differences between groups. The analysis of the data showed differences in all the patterns of the ensemble mean curves of the HIGH and LOW groups. During the eccentric phase, the differences in the HIGH group with respect to the LOW group were: Lower crouch position, higher downward velocity, and higher force and rate of force development during the braking of the downward movement. During the concentric phase, the HIGH group achieved higher upward velocity, higher force at the end of phase, and a higher position at take-off. The higher jump performances appear to be related to a more effective stretch-shortening cycle function which is characterized by a deeper and faster counter-movement with higher eccentric forces being applied to decelerate the downward movement leading to enhanced force generation during the concentric phase.Abstract Word Count: 198

Is a Bimodal Force-Time Curve Related to Countermovement Jump Performance?

“…Vertical jumps are routinely used to test the force production and many other output related capacities of the lower body extensor muscles [ 18 , 19 ]. The results from the present study support the previous literature [ 1 , 34 , 37 , 38 , 39 ] in that braking COM displacement does exert a meaningful influence on kinetic variables, such as propulsion peak force ( Table 1 ).…”

“…Temporal phase analysis of a force-time curve is often considered a useful tool to improve our understanding of CMJ performance and is often applied in cross-sectional examinations [ 14 , 18 ]. When the subjects in the current study were stratified by jump height, substantial differences were only identified during the late propulsion phase—79.0% to 97.0% of the normalised force-time curve ( Figure 1 ).…”

“…The vertical velocity of the COM at take-off was used to calculate jump height. To normalize for physical size, force and displacement variables were scaled to body mass and leg length, respectively [ 18 ]. To examine the shape of the force-time curve, the propulsion phase was divided into three sub-phases of equal duration [ 26 ].…”

“…Normalization of a curve’s amplitude is routinely done by scaling the variable of choice with an appropriate physical constant, such as body mass or leg length [ 18 ]. In addition, some type of temporal phase analysis is required to facilitate comparisons between and within individuals, usually by expressing the data as a percentage of the total duration [ 17 ].…”

“…When jump durations are aligned by LLN, temporal differences between points of interest may still exist due to inter-individual variability and are recognised to slightly confound point-by-point comparisons. However, pooling unimodal and bimodal shaped force-time curves, as is routinely completed [ 5 , 7 , 14 , 18 , 19 , 20 , 21 , 22 ], may lead to misinterpretation of the data. An additional concern is that the null-hypothesis test has been the universal approach to inferential statistics in these studies, and as such, a statistically significant difference in a force-time curve may represent an effect that is practically meaningless, or conversely, a non-significant difference may indeed have a meaningful effect [ 23 ].…”