UNLABELLED: Time trials are commonly used in the lead-up to competition. A method that evaluates the relationship between time trial and competition performance in swimming would be useful for developing performance-enhancement strategies. PURPOSE: To use linear mixed modeling to identify key parameters that can be used to relate time-trial and competition performance. METHODS: Ten swimmers participated in the study. Each swimmer was analyzed during 3 time trials and 1 competition. Race video footage was analyzed to determine several key parameters. Pooling of strokes and distances was achieved by modeling changes in parameters between time trials and competition within each subject as linear predictors of percent change in performance using mixed modeling of log-transformed race times. RESULTS: When parameters were evaluated as the effect of 2 SD on performance time, there were very large effects of start time (2.6%, 90% confidence interval 1.8-3.3%) and average velocity (-2.3%, -2.8% to -1.8%). There was also a small effect for stroke rate (-0.6%, -1.3% to 0.2%). Further analysis revealed an improvement in performance time of 2.4% between time trials and competition, of which 1.8% (large; 1.4-2.1%) was due to a change in average velocity and 0.9% (moderate; 0.6-1.1%) was due to a change in start time; changes in remaining parameters had trivial effects on performance. CONCLUSION: This study illustrates effective analytical strategies for identifying key parameters that can be the focus of training to improve performance in small squads of elite swimmers and other athletes.
UNLABELLED: Time trials are commonly used in the lead-up to competition. A method that evaluates the relationship between time trial and competition performance in swimming would be useful for developing performance-enhancement strategies. PURPOSE: To use linear mixed modeling to identify key parameters that can be used to relate time-trial and competition performance. METHODS: Ten swimmers participated in the study. Each swimmer was analyzed during 3 time trials and 1 competition. Race video footage was analyzed to determine several key parameters. Pooling of strokes and distances was achieved by modeling changes in parameters between time trials and competition within each subject as linear predictors of percent change in performance using mixed modeling of log-transformed race times. RESULTS: When parameters were evaluated as the effect of 2 SD on performance time, there were very large effects of start time (2.6%, 90% confidence interval 1.8-3.3%) and average velocity (-2.3%, -2.8% to -1.8%). There was also a small effect for stroke rate (-0.6%, -1.3% to 0.2%). Further analysis revealed an improvement in performance time of 2.4% between time trials and competition, of which 1.8% (large; 1.4-2.1%) was due to a change in average velocity and 0.9% (moderate; 0.6-1.1%) was due to a change in start time; changes in remaining parameters had trivial effects on performance. CONCLUSION: This study illustrates effective analytical strategies for identifying key parameters that can be the focus of training to improve performance in small squads of elite swimmers and other athletes.
Authors: Klaus Wirth; Michael Keiner; Stefan Fuhrmann; Alfred Nimmerichter; G Gregory Haff Journal: Int J Environ Res Public Health Date: 2022-04-28 Impact factor: 4.614