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Modeling and predicting the backstroke to breaststroke turns performance in age-group swimmers

2021; Taylor & Francis; Volume: 22; Issue: 12 Linguagem: Inglês

10.1080/14763141.2021.2005127

1752-6116

Phornpot Chainok, Karla de Jesus, Leandro dos Santos Coelho, Helon Vicente Hultmann Ayala, Mateus Gheorghe de Castro Ribeiro, Ricardo J. Fernandes, João Paulo Vilas‐Boas,

Sports injuries and prevention

The purpose of the present study was to identify the performance determinant factors predicting 15-m backstroke-to-breaststroke turning performance using and comparing linear and tree-based machine-learning models. The temporal, kinematic, kinetic and hydrodynamic variables were collected from 18 age-group swimmers (12.08 ± 0.17 yrs) using 23 Qualisys cameras, two tri-axial underwater force plates and inverse dynamics approach. The best models were obtained: (i) with Lasso linear model of the leave-one-out cross-validation in open turn (MSE = 0.011; R2 = 0.825) and in the somersault turn (MSE = 0.016; R2 = 0.734); (ii) the Ridge of the leave-one-out cross-validation (MSE = 0.016; R2 = 0.763) for the bucket turn; and (iii) the AdaBoost tree-based model of the leave-one-out cross-validation for the crossover turn (MSE = 0.016; R2 = 0.644). Model's selected features revealed that optimum turning performance was very similarly determined for the different techniques, with balanced contributions between turn-in and turn-out variables. As a result, the relevant feature's contribution of each backstroke-to-breaststroke turning technique are specific; developing approaching speed in conjunction with proper gliding posture and pull-out strategy will result in improved turning performance, and may influence differently the development of specific training intervention programmes.