PURPOSE: To analyze the reproducibility of pacing in elite swimmers during competitions and to compare heats and finals within 1 event. METHODS: Finals and heats of 158 male swimmers (age 22.8 ± 2.9 y) from 29 nations were analyzed in 2 competitions (downloaded from swimrankings.net). Of these, 134 were listed in the world's top 50 in 2010; the remaining 24 were finalists of the Pan Pacific Games or European Championships. The level of both competitions for the analysis had to be at least national championships (7.7 ± 5.4 wk apart). Standard error of measurement expressed as percentage of the subject's mean score (CV) with 90% confidence limits (CL) for each 50-m split time and for total times were calculated. In addition, mixed general modeling was used to determine standard deviations between and within swimmers. RESULTS: CV for total time in finals ranged between 0.8% and 1.3% (CL 0.6-2.2%). Regarding split times, 200-m freestyle showed a consistent pacing over all split times (CV 0.9-1.6%). During butterfly, backstroke, and 400-m freestyle, CVs were low in the first 3 and 7 sections, respectively (CV 0.9-1.7%), with greater variability in the last section (1.9-2.2%). In breaststroke, values were higher in all sections (CV 1.2-2.3%). Within-subject SDs for changes between laps were between 0.9% and 2.6% in all finals. Split-time variability for finals and heats ranged between 0.9% and 2.5% (CL 0.3-4.9%). CONCLUSION: Pacing profiles are consistent between different competitions. Variability of pacing seems to be a result of the within-subject variation rather than a result of different competitions.
PURPOSE: To analyze the reproducibility of pacing in elite swimmers during competitions and to compare heats and finals within 1 event. METHODS: Finals and heats of 158 male swimmers (age 22.8 ± 2.9 y) from 29 nations were analyzed in 2 competitions (downloaded from swimrankings.net). Of these, 134 were listed in the world's top 50 in 2010; the remaining 24 were finalists of the Pan Pacific Games or European Championships. The level of both competitions for the analysis had to be at least national championships (7.7 ± 5.4 wk apart). Standard error of measurement expressed as percentage of the subject's mean score (CV) with 90% confidence limits (CL) for each 50-m split time and for total times were calculated. In addition, mixed general modeling was used to determine standard deviations between and within swimmers. RESULTS: CV for total time in finals ranged between 0.8% and 1.3% (CL 0.6-2.2%). Regarding split times, 200-m freestyle showed a consistent pacing over all split times (CV 0.9-1.6%). During butterfly, backstroke, and 400-m freestyle, CVs were low in the first 3 and 7 sections, respectively (CV 0.9-1.7%), with greater variability in the last section (1.9-2.2%). In breaststroke, values were higher in all sections (CV 1.2-2.3%). Within-subject SDs for changes between laps were between 0.9% and 2.6% in all finals. Split-time variability for finals and heats ranged between 0.9% and 2.5% (CL 0.3-4.9%). CONCLUSION: Pacing profiles are consistent between different competitions. Variability of pacing seems to be a result of the within-subject variation rather than a result of different competitions.
Authors: Daniel A Marinho; Tiago M Barbosa; Henrique P Neiva; António J Silva; Jorge E Morais Journal: J Sports Sci Med Date: 2020-05-01 Impact factor: 2.988