DISCRIMINANT ANALYSIS OF ANTHROPOMETRIC AND BIOMOTOR VARIABLES AMONG GROUPS OF MALE UNIVERSITY ATHLETES IN THREE SPORTS
DOI:
https://doi.org/10.46827/ejpe.v0i0.2040Keywords:
talent identification, strength, power, body composition, linear discriminant analysis (LDA), anthropometric, bio-motor, kinanthropometry, multivariate analysisAbstract
The aim of this study was to identify anthropometric and bio-motor variables that discriminated among groups of university male athletes aged 21.4 ± 2.1 years (mean ± s) from three different sports (soccer, n = 53; rugby, n = 27; basketball, n = 31). Anthropometric parameters included body mass, height and body fat percentage. The bio-motor variables included blood pressure and maximal aerobic power (VO2max) measured using 40meter dash test. Using a cross-sectional design, discriminant analysis was used to determine field tests identifying male university athletes. Young men (n = 111) who were to participate in the East Africa Inter-university games volunteered as subjects (mean ± SD age = 21.4 ± 2.1 years). Discriminant analysis revealed two significant functions (P < 0.05). After validation, the resulting regression equations correctly classified 83.8% of the soccer, rugby and basketball athletes. Six variables significantly contributed to the discriminant analysis (Wilks A = 0.231, x2 = 153.95, df = 14, p < 0.0001, adjusted R2 = 0.788). The interpretation of the obtained discriminant functions was also based on examination of the structure coefficients greater than 0.30. The athletes were discriminated mainly on height (structure coefficient, SC=0.319), percentage body fat (SC=0.401), 40-meter dash (SC=0.482), upper body strength (SC=0.404) and abdominal strength (SC=0.253). Our model confirms that university male athletes show physical and bio-motor differences that clearly distinguish them according to their particular sport. Generally, the athletes’ anthropometric and bio-motor variables varied according to sport, probably because of the different training regimens in the different sports that conditioned the athletes differently. In conclusion, these discriminant models could help in player recruitment and improve training programs.
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