Construction of an anthropometric discriminant model for identification of elite swimmers: an adaptive lasso approach

BACKGROUND: Anthropometric characteristics are important factors that affect swimming performance. The aim of this study is to build a discriminant model using anthropometric factors to identify elite short-to-medium-distance freestyle swimmers through an adaptive Lasso approach. METHODS: The study recruited 254 swimmers (145 males and 109 females) who were divided them into elite (aged 17.9 ± 2.2 years, FINA points 793.8 ± 73.8) and non-elite (aged 17.1 ± 1.3 years, FINA points 560.6 ± 78.7) groups. Data for 73 variables were obtained, including basic information, anthropometric and derivative indicators. After filtering out highly correlated variables, 24 candidate variables were retained to be used in adaptive Lasso to select variables for prediction of elite swimmers. Deviance and area under the curve (AUC) were applied to assess the goodness of fit and prediction accuracy of the model, respectively. RESULTS: The adaptive Lasso selected 12 variables using the whole sample, with an AUC being 0.926 (95% CI [0.895–0.956]; P = 2.42 × 10(−29)). In stratified analysis by gender, nine variables were selected for male swimmers with an AUC of 0.921 (95% CI [0.880–0.963]; P = 8.82 × 10(−17)), and eight variables were for female swimmers with an AUC of 0.941 (95% CI [0.898–0.984]; P = 7.67 × 10(−15)). CONCLUSION: The adaptive Lasso showed satisfactory performance in selecting anthropometric characteristics to identify elite swimmers. Additional studies with longitudinal data or data from other ethnicities are needed to validate our findings.