100m and 200m front crawl performance prediction based on anthropometric and physiological measurements

• Autor(a) principal: Reis, Victor M.
• Data de Publicação: 2012
• Outros Autores: Silva, A.J., Carneiro, André Luiz, Marinho, D.A., Novaes, Giovanni Silva, Barbosa, Tiago M.
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10198/8322
• topic: Competitive swimming; Front crawl; Prediction; Performance; Energetic; Anthropometrics

The identification of the variables that are able to predict swimming performance is one of the main purposes of the “swimming science” community. Research question: The aims of the study were: (i) to compare the anthropometric and physiological profiles of 100m and 200m front crawl swimmers and; (ii) to identify anthropometric and physiological variables that account for the prediction of the swimming performance at the 100m and 200m front crawl events. Methods: Twenty-six male swimmers were divided in two groups (12 for 100m group and 14 to 200m group). The swimmers’ personal best performance for the 100m and the 200m front crawl was converted to FINA points. The subjects performed a graded swimming test and an all-out test (100 or 200m maximal swims) in different days, in which physiological measures were evaluated. Forward step-by-step linear regression models were computed to predict swimming performance. The subjects’ performances (season best and all-out test) were taken as dependent variables. The age, physiological and anthropometric measures were selected as independent variables. Results: Anthropometric and physiological profiles of 100 and 200m swimmers are different and the mean oxygen uptake during exercise combined with training experience may explain 200m front crawl best season performance with a high precision (≈2% error). The models computed were able to predict from 44 % (i.e. 200m all-out bout) to 61 % (i.e. 200m season best) swimming performance. Predictive power of the models was less accurate in the 100m event (error > 10%). Conclusions: The authors conclude that the extent to which the physiological and anthropometric variables combine to predict performance probable is group-specific.