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  • Title: Anthropometric Parameters, Age, and Agility as Performance Predictors in Elite Female Basketball Players.
    Author: Garcia-Gil M, Torres-Unda J, Esain I, Duñabeitia I, Gil SM, Gil J, Irazusta J.
    Journal: J Strength Cond Res; 2018 Jun; 32(6):1723-1730. PubMed ID: 29786629.
    Abstract:
    Garcia-Gil, M, Torres-Unda, J, Esain, I, Duñabeitia, I, Gil, SM, Gil, J, and Irazusta, J. Anthropometric parameters, age, and agility as performance predictors in elite female basketball players. J Strength Cond Res 32(6): 1723-1730, 2018-In addition to technical, tactical, and psychological skills, performance in playing basketball depends on anthropometry and physical fitness. However, limited information is available regarding such features in women. We hypothesized that anthropometry and physical fitness are associated with female basketball performance, and consequently, performance could be predicted using the results of certain anthropometric measures and fitness tests. Body parameters (age, height, body mass, skinfold thicknesses, limb perimeters, and lengths) were measured. Physical fitness capacities (jumping, agility with and without the ball, and speed) were measured by specific tests. In addition, game performance was assessed using technical statistics (rebounds, assists, and points) and the performance index rating (PIR). Teams ranked better in the regular season had smaller mean fat skinfold thickness and spent less time in the agility tests (T-Drill). Correlation analyses indicated that players with better PIR were older, taller, and had a longer arm span and greater contracted arm perimeter (CAP). Furthermore, those players had better results in the T-Drill test. Multiple regression analysis indicated that combined age, height, CAP, fat skinfold thickness, and time in T-Drill test yielded a strong predictor of PIR per time played. In conclusion, the results of the present study indicate that some anthropometric and physical fitness characteristics of female elite basketball teams and players are highly associated with performance-related parameters. In addition, a regression model has been developed to predict the performance of female basketball players.
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