100 m backstroke time estimation from age: A model based on regession analysis - Morales, E.; Dominguez-Castells, R.; Femia, P.; Arellano, R. (17th Annual Congress of the European College of Sport Science (ECSS), Bruges, 4. -7. July 2012, 2012)
The dynamic process of training needs as much information as possible from performance and training testing, which will help the coach monitor the training program. It must be designed according to swimming time and age. Performance in 100 m backstroke (B) improves with age during the growing period. Race components change with gradual development of many physical fitness factors. Regression equations were obtained through the race components analysis in different competitions (Absaliamov and Timakovoy, 1990; Arellano et al., 1996; Nomura, 2006). This investigation aimed to estimate the event times (ET) in 100m backstroke from age. Methods: Data from different competitions (Age group Andalusia Championship 2004, European Youth Olympics 2001, Junior European Championship 2000 to 2004, and Senior European Championship 2000 to 2005) were used to develop the regression analysis. These times were published in a web: http://www.swim.ee//competition. 100 m backstroke semifinalists and finalists were analyzed. The Kolmogorov-Smirnov test, which confirmed the normal distribution, was used. Regression analysis was used to determine the tendency and model of the event time. Inverse function approximation of the event time by age (AGE) and gender (GEN) was carried out. The generic equation obtained was: ET = a + a´ * GEN + b/AGE (1 - b´ * GEN) The time estimation formula from age according to gender was as follows: ET men = a1 + b1/ AGE; ET woman = a2 + b2/AGE Furthermore, regression analysis and inverse function for the event time were calculated. Results: The type of equations obtained for event time according to gender were inverse functions: ET men = 26.752+556.094* AGE -1 (R2 = 0.646); ET woman = 35.852+458.654* AGE -1 (R2 = 0.593) Discussion: 200m freestyle times for young swimmers during growth were estimated through regression analysis (Nomura, 2006). The model proposed for backstroke enhances this estimation, allowing to determine swimming times according to age and gender. Peak performance times were estimated until 35 years old (maximum swimmers´age found in these competitions). A progressive time increase until 70 years old was obtained in the analysis of different competitions in the US Masters Swimming, which was characterized by a steep quadratic term (Donato et al., 2003). In conclusion, bearing long-term planning in mind, with internal and external development factors, this regression model can be used as a reference guide for a correct swimmer development.