This study develops multivariate models to predict swimming performance based on multidimensional assessment. 66 male (age 13.6 ± 0.6 y) and 67 female (11.5 ± 0.6 y) swimmers undertook a test battery including a sports background and training questionnaire, anthropometry, general and specific fitness tests, and technique. Competitive performance (LEN scores in three best events) was the predicted variable. A multiple linear regression model explained 82.4% of performance variability in males (based on age, sitting height, 30-min test, 6 × 50 m at 1:30, and swimming index) and 84.5% in females (age, 30-min test, 6 × 50 m at 1:30, and velocity at 50 m). Discriminant analysis using a four-group split-sample approach correctly classified 94.1% of the best male swimmers (based on age, 30-min test, 6 × 50 m at 1:30, shoulder extension, arm span, and height), and 71.0% of the best females swimmers (30-min test, horizontal floating, velocity at 50 m, and age). Chronological age was the main predictor of performance in this age category. Main predictive variables pertained to the anthropometric (particularly in males), specific fitness (aerobic speed and endurance), and technical domains (particularly in females). In these ages competitions should be organized according to year of birth and not by age categories.
Saavedra and Escalante are with the University of Extremadura, Cáceres, Spain. Rodríguez is with the University of Barcelona, Barcelona, Spain.