Key determinants of time to 5 m in different ventral swimming start techniques

Abstract

The aim of this study was to determine the biomechanical parameters that explain ventral start performance in swimming. For this purpose, 13 elite swimmers performed different variants of the ventral start technique. Two-dimensional video analyses of the aerial and underwater phases were used to assess 16 kinematic parameters from the starting signal to 5 m, and an instrumented starting block was used to assess kinetic data. A Lasso regression was used to reduce the number of parameters, providing the main determinants to starting performance, revealing different combinations of key determinants, depending on the variant (r² ≥ 0.90), with flight distance being the most relevant to all variants (r ≤ -0.80; p < .001). Also, special attention should be given to the total horizontal impulse in the grab start (r = -0.79; p < .001) and to the back foot action in the track and kick starts (r ≤ 0.61; p < .001). In addition, we provide two equations that could be easily used to predict starting performance by assessing block time and flight time (r² = 0.66) or block time and flight distance (r² = 0.83). These data provide relevant contributions to the further understanding of the biomechanics of swimming starts as well as insights for performance analysis and targeted interventions to improve athlete performance.

Key Factors Related to Short Course 100 m Breaststroke Performance

“…The importance of the flight distance was consistent with Peterson Silveira et al [ 8 ], who reported a strong relationship (r ≈ −0.895) between the flight distance in front and rear kick starts and the 5 m time. The peak velocity investigated in the present study was the peak head velocity and not the center of mass velocity.…”

“…Previous research has identified factors contributing to the time spent in these segments, e.g., start (block position, reaction/block time, push-off, flight, entry and underwater) [ 6 , 7 , 8 , 9 ], turn (5 m approach, pivot time, push-off, underwater distance and velocity) [ 10 , 11 , 12 ], clean swimming velocity (cycle length and cycle rate) [ 13 , 14 ], and the finish (timing of the wall touch and the trajectory of the hands) [ 15 ]. Therefore, the winner of a race or the swimmer with the fastest finishing time is not always the swimmer with the highest mean clean swimming velocity, but rather the swimmer who performs well on all of the race segments [ 2 ].…”

Predicting dive start performance from kinematic variables at water entry in (sub-)elite swimmers

“…Although useful insights into relevant performance determining variables may be obtained in this manner, these distances, and the associated times, do not correspond to the aforementioned start phases. Since swimmers enter the water well before crossing the 5-m line [ 20 , 21 ], the time required to do so is also dependent on the characteristics of the underwater phase [ 22 ], and the same holds for the 7.5-m and 10-m line. As a result, using this approach, it is difficult to gain a clear understanding of how the actions in one phase affect the actions in the next, and how the overall start performance results from the performance in each phase.…”

“…There are also strong empirical grounds for adopting a focus on entry state to better understand the dive start. Previous research has identified several kinematic variables pertaining to the swimmer’s state at water entry that are associated with dive start performance, including the swimmer’s distance from the block [ 10 , 20 , 21 , 27 ], the horizontal velocity of the swimmer’s centre of mass [ 9 , 10 , 24 ], and the swimmer’s angle with the water surface [ 21 ]. That these variables contribute to start performance can be readily understood from basic mechanical considerations.…”

“…Firstly, it is not only important to determine which (potential) performance-related variables contribute to start performance, but also their relative contribution compared to other variables. This requires not only that multiple rather than simple regression techniques are employed, as has been done in several previous studies (e.g., [ 20 , 24 ], but also that the analysis is applied to a data set with sufficient variation in the variables of interest, such that meaningful statistical patterns can be reliably detected and quantified. To achieve this, larger than usual variations in the swim starts need to be induced experimentally.…”