Race level comparison and variability analysis of 100 m freestyle sprinters competing in the 2019 European championships

Abstract

This study aimed to (i) compare the race performance of the swimmers with better performances and poorer performances during all sections of a 100 m freestyle event and (ii) compare stroke kinematics variables between tiers and analyse their stability in each tier. The sample was composed of 88 swimmers that participated in the 100 m Freestyle event at the 2019 LEN European Junior Championships. Speed achieved the largest difference between tiers in section (S) S0-15 m of lap #1 (mean difference = −0.109 s, p < 0.001). During the clean swim and finish phases, the stroke length and stroke index presented significant differences (p < 0.05) between tiers in all sections of the race (stroke frequency did not). Significant variances were noted for both tiers in all variables in both laps. Swimmers in tier #1 were significantly faster than swimmers in tier #2 especially in sections related to the push-off against a solid (block or wall), and finish. A significant variance was noted by both tiers during the race with a moderate-to-high normative stability. Coaches are advised to analyse and understand the swimmers' within-lap stability, which can give deeper details about their swimmers' behaviour during the 100 m freestyle race.

Race analysis in swimming: understanding the evolution of publications, citations and networks through a bibliometric review

“…There have also been research groups that have performed race analyses based on video footage obtained and approved by official European organizers ( 11 , 67 ). These analyses provided deeper insights into the swimmers' behavior in real competition contexts regarding all phases of the swim race, thus adding to the body of knowledge on this topic ( 57 , 65 ). As a result, the number of citations was based on the number of articles published.…”

“…Sports science emerged as the dominant category, accounting for 81.3% of the publications examined. Notable examples include articles such as (55) (63), and (73), which represent the pivotal role of sports science in the study of various dimensions of swimming performance, ranging from biomechanical analysis to training methodologies and performance evaluation. Furthermore, the presence of interdisciplinary connections is evident, as evidenced by articles such as (43) and (72) in biomedical engineering (30), in physiology, and ( 39) and (50) in environmental sciences.…”

Association between elite swimmers’ force production and 100 m front crawl inter-lap pacing and kinematics

“…Cohen's d was selected as effect size and interpreted as trivial if d < 0.2, medium if 0.2 > d < 0.5, and large if d ≥ 0.5 (22). Pearson correlation coefficients (r) were determined between force and remaining variables, being thereafter interpreted as low if r < 0.30, moderate if 0.30 ≥ r < 0.60, and reasonably good if r ≥ 0.60 (23). Scatter plots with individual values and 95% confidence limits were computed to illustrate associations.…”