Effects of anthropometrics, thrust, and drag on stroke kinematics and 100 m performance of young swimmers using path‐analysis modeling

Author:

Morais Jorge E.12ORCID,Barbosa Tiago M.12,Gomeñuka Natalia A.3,Marinho Daniel A.45

Affiliation:

1. Department of Sport Sciences Instituto Politécnico de Bragança Bragança Portugal

2. Research Centre for Active Living and Wellbeing (LiveWell), Instituto Politécnico de Bragança Bragança Portugal

3. Research Department of the Faculty of Health Sciences, Universidad Católica de las Misiones (UCAMI) Posadas Argentina

4. Department of Sport Sciences University of Beira Interior Covilhã Portugal

5. Research Centre in Sports, Health and Human Development (CIDESD) Covilhã Portugal

Abstract

AbstractThe aim of this study was to understand the interactions between anthropometric, kinetic, and kinematic variables and how they determine the 100 m freestyle performance in young swimmers. Twenty‐five adolescent swimmers (15 male and 10 female, aged 15.75 ± 1.01 years) who regularly participated in regional and national competitions were recruited. The 100 m freestyle performance was chosen as the variable to be predicted. A series of anthropometric (hand surface area–HSA), kinetic (thrust and active drag coefficient (CDA)), and kinematic (stroke length (SL); stroke frequency (SF), and swimming speed) variables were measured. Structural equation modeling (via path analysis) was used to develop and test the model. The initial model predicted performance with 90.1% accuracy. All paths were significant (p < 0.05) except the thrust—SL. After deleting this non‐significant path (thrust—SL) and recalculating, the model goodness‐of‐fit improved and all paths were significant (p < 0.05). The predicted performance was 90.2%. Anthropometrics had significant effects on kinetics, which had significant effects on kinematics, and consequently on the 100 m freestyle performance. The cascade of interactions based on this path‐flow model allowed for a meaningful prediction of the 100 m freestyle performance. Based on these results, coaches and swimmers should be aware that the swimming predictors can first meaningfully interact with each other to ultimately predict the 100 m freestyle performance.

Funder

Fundação para a Ciência e a Tecnologia

Publisher

Wiley

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