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Interaction of Kinematic, Kinetic, and Energetic Predictors of Young Swimmers' Speed.
PURPOSE: The aim of this study was to assess the interaction of kinematic, kinetic, and energetic variables as speed predictors in adolescent swimmers in the front-crawl stroke.
DESIGN: Ten boys (mean age [SD] = 16.4 [0.7] y) and 13 girls (mean age [SD] = 14.9 [0.9] y) were assessed.
METHODS: The swimming performance indicator was a 25-m sprint. A set of kinematic, kinetic (hydrodynamic and propulsion), and energetic variables was established as a key predictor of swimming performance. Multilevel software was used to model the maximum swimming speed.
RESULTS: The final model identified time (estimate = -0.008, P = .044), stroke frequency (estimate = 0.718, P < .001), active drag coefficient (estimate = -0.330, P = .004), lactate concentration (estimate = 0.019, P < .001), and critical speed (estimate = -0.150, P = .035) as significant predictors. Therefore, the interaction of kinematic, hydrodynamic, and energetic variables seems to be the main predictor of speed in adolescent swimmers.
CONCLUSIONS: Coaches and practitioners should be aware that improvements in isolated variables may not translate into faster swimming speed. A multilevel evaluation may be required for a more effective assessment of the prediction of swimming speed based on several key variables rather than a single analysis.
DESIGN: Ten boys (mean age [SD] = 16.4 [0.7] y) and 13 girls (mean age [SD] = 14.9 [0.9] y) were assessed.
METHODS: The swimming performance indicator was a 25-m sprint. A set of kinematic, kinetic (hydrodynamic and propulsion), and energetic variables was established as a key predictor of swimming performance. Multilevel software was used to model the maximum swimming speed.
RESULTS: The final model identified time (estimate = -0.008, P = .044), stroke frequency (estimate = 0.718, P < .001), active drag coefficient (estimate = -0.330, P = .004), lactate concentration (estimate = 0.019, P < .001), and critical speed (estimate = -0.150, P = .035) as significant predictors. Therefore, the interaction of kinematic, hydrodynamic, and energetic variables seems to be the main predictor of speed in adolescent swimmers.
CONCLUSIONS: Coaches and practitioners should be aware that improvements in isolated variables may not translate into faster swimming speed. A multilevel evaluation may be required for a more effective assessment of the prediction of swimming speed based on several key variables rather than a single analysis.
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