Biophysical Follow-up of Age-Group Swimmers During a Traditional Three-Peak Preparation Program.

Zacca, R, Azevedo, R, Ramos, VR, Abraldes, JA, Vilas-Boas, JP, Castro, FAdS, Pyne, DB, and Fernandes, RJ. Biophysical follow-up of age-group swimmers during a traditional three-peak preparation program. J Strength Cond Res 34(9): 2585-2595, 2020-The aim of this study was to quantify changes and contributions of bioenergetic, technique, and anthropometric profiles across a traditional 3-peak swimming season. Twenty-four age-group swimmers (11 boys: 15 years 6 months ± 1 year 1 month; 13 girls: 14 years 5 months ± 10 months) of equal maturational stage were monitored through a 400-m test in front crawl (T400). Bioenergetic, technique, and anthropometric characteristics were compared before and after macrocycles I, II and III. Sex interaction was verified only for amplitude of the fast oxygen uptake component and height (moderate). Multiple linear regressions and principal component analysis were used to identify the most influential variables and the relative contribution of each domain (bioenergetics, technique, and anthropometrics) to changes in swimming performance of T400. The relative contributions for the performance of T400 after macrocycles I, II, and III were, respectively, 6, 18, and 27% for bioenergetics, 88, 69, and 54% for technique, and 6, 13, and 20% for anthropometrics. Technique was the biggest contributor (71%) for changes in the performance of T400 over the training season, followed by bioenergetics (17%) and anthropometrics (12%). Technique played the main role during the competitive season, regardless of gradual increase in the contribution of bioenergetics and anthropometrics. Despite that, bioenergetics and technique are closely connected, thus a powerful and endurable metabolic base and cannot be overlooked. Changes and contribution of bioenergetics, technique, and anthropometrics on age-group swimmers' performance over a traditional 3-peak swimming season could be described by the T400 swimming test, providing a comprehensive biophysical overview of the main contributors to swimming performance.

Modelling and Predicting Backstroke Start Performance Using Non-Linear and Linear Models.

Our aim was to compare non-linear and linear mathematical model responses for backstroke start performance prediction. Ten swimmers randomly completed eight 15 m backstroke starts with feet over the wedge, four with hands on the highest horizontal and four on the vertical handgrip. Swimmers were videotaped using a dual media camera set-up, with the starts being performed over an instrumented block with four force plates. Artificial neural networks were applied to predict 5 m start time using kinematic and kinetic variables and to determine the accuracy of the mean absolute percentage error. Artificial neural networks predicted start time more robustly than the linear model with respect to changing training to the validation dataset for the vertical handgrip (3.95 ± 1.67 vs. 5.92 ± 3.27%). Artificial neural networks obtained a smaller mean absolute percentage error than the linear model in the horizontal (0.43 ± 0.19 vs. 0.98 ± 0.19%) and vertical handgrip (0.45 ± 0.19 vs. 1.38 ± 0.30%) using all input data. The best artificial neural network validation revealed a smaller mean absolute error than the linear model for the horizontal (0.007 vs. 0.04 s) and vertical handgrip (0.01 vs. 0.03 s). Artificial neural networks should be used for backstroke 5 m start time prediction due to the quite small differences among the elite level performances.

Predicting playing status in professional water polo players: analysis by gender.

BACKGROUND: The aim of this study was twofold: firstly, to identify the characteristics of water polo players that discriminate between women and men based on specific playing positions; and secondly to develop a predicting model to identify the characteristics that are best suited to a given playing position based on gender. METHODS: The study included 130 professional water polo players. Anthropometric characteristics and throwing velocity were analyzed in different situations: no defender or goalkeeper, goalkeeper only and the players made three rapid arm movements and then threw the ball at maximum speed without further feinting with goalkeeper. Measured variables were compared according to gender and player position using discriminant analysis. RESULTS: The predictive model accurately classifies 71.1% of the male players using three variables (arm span, muscle mass Lee and penalty without goalkeeper), and 64.7% of the female players using three variables (triceps skinfold, biceps skinfold and anteroposterior chest breadth). CONCLUSIONS: The anthropometric characteristics and throwing velocity play an important role in identifying the different specific positions in male and female water polo players. In female players, the variables that were determinant in the predictive model, were those associated with body composition.