Selection-Dependent Differences in Youth Elite Basketball Players’ Relative Age, Maturation-Related Characteristics, and Motor Performance

One possible reason for the continued neglect of statistical power analysis in research in the behavioral sciences is the inaccessibility of or difficulty with the standard material. A convenient, although not comprehensive, presentation of required sample sizes is provided here. Effect-size indexes and conventional values for these are given for operationally defined small, medium, and large effects. The sample sizes necessary for .80 power to detect effects at these levels are tabled for eight standard statistical tests: (a) the difference between independent means, (b) the significance of a product-moment correlation, (c) the difference between independent rs, (d) the sign test, (e) the difference between independent proportions, (f) chi-square tests for goodness of fit and contingency tables, (g) one-way analysis of variance, and (h) the significance of a multiple or multiple partial correlation. Show more

The range of variability between individuals of the same chronological age (CA) in somatic and biological maturity is large and especially accentuated around the adolescent growth spurt. Maturity assessment is an important consideration when dealing with adolescents, from both a research perspective and youth sports stratification. A noninvasive, practical method predicting years from peak height velocity (a maturity offset value) by using anthropometric variables is developed in one sample and cross-validated in two different samples. Gender specific multiple regression equations were calculated on a sample of 152 Canadian children aged 8-16 yr (79 boys; 73 girls) who were followed through adolescence from 1991 to 1997. The equations included three somatic dimensions (height, sitting height, and leg length), CA, and their interactions. The equations were cross-validated on a combined sample of Canadian (71 boys, 40 girls measured from 1964 through 1973) and Flemish children (50 boys, 48 girls measured from 1985 through 1999). The coefficient of determination (R2) for the boys' model was 0.92 and for the girls' model 0.91; the SEEs were 0.49 and 0.50, respectively. Mean difference between actual and predicted maturity offset for the verification samples was 0.24 (SD 0.65) yr in boys and 0.001 (SD 0.68) yr in girls. Although the cross-validation meets statistical standards for acceptance, caution is warranted with regard to implementation. It is recommended that maturity offset be considered as a categorical rather than a continuous assessment. Nevertheless, the equations presented are a reliable, noninvasive and a practical solution for the measure of biological maturity for matching adolescent athletes Show more

The physical demands of modern basketball were assessed by investigating 38 elite under-19-year-old basketball players during competition. Computerised time-motion analyses were performed on 18 players of various positions. Heart rate was recorded continuously for all subjects. Blood was sampled before the start of each match, at half time and at full time to determine lactate concentration. Players spent 8.8% (1%), 5.3% (0.8%) and 2.1% (0.3%) of live time in high "specific movements", sprinting and jumping, respectively. Centres spent significantly lower live time competing in high-intensity activities than guards (14.7% (1%) v 17.1% (1.2%); p<0.01) and forwards (16.6% (0.8%); p<0.05). The mean (SD) heart rate during total time was 171 (4) beats/min, with a significant difference (p<0.01) between guards and centres. Mean (SD) plasma lactate concentration was 5.49 (1.24) mmol/l, with concentrations at half time (6.05 (1.27) mmol/l) being significantly (p<0.001) higher than those at full time (4.94 (1.46) mmol/l). The changes to the rules of basketball have slightly increased the cardiac efforts involved during competition. The game intensity may differ according to the playing position, being greatest in guards. Show more