## 3 Results
Figure 2A shows the mean vectors of the basketball players grouped by age category and MS on the R-Xc graph of the reference values for an athletic population [19], while in  
Figure 1: The somatchart shows the average somatotypes of athletes of different age groups. Dashed lines represent the middle type and explain the body characteristics of the vertex features: players lie around the mesomorph-ectomorphic type. Figure 2B shows that early maturers of U15 entered the 50\({}^{\circ}\) percentile of the basketball reference population, on-time maturers of U15 entered the 75\({}^{\circ}\) percentile, and early maturers of U13 the 95\({}^{\circ}\) percentile. The early maturers of U14 were placed near the ellipses but not inside. A group further away from the ellipses was represented by the categories early maturers of U12 and on-time maturers of U13 and U14, while on-time U12 members were even further apart. Figure 3A shows the relationship between the BIA PhA and the interaction of age categories and mesomorphy. The slopes show a linear trend with ageing and increments in muscle-skeletal systems [U13 vs. U12: contrast = \(-\)0.095 (\(-\)0.30, 0.11), F = 0.87, \(p\) = 0.354; U14 vs. <U14: contrast = 0.179 (\(-\)0.07; 0.43), F = 2.01, \(p\) = 0.158; U15 vs. <U15: contrast = 0.789 (0.52; 1.06), F = 32.98, \(p\) < 0.001; U17 vs. <U17: contrast = 1.00 (0.75; 1.26),  
Figure 2: (**A**) Mean vectors of the basketball players grouped by age category and maturity status on the R–Xc graph of the reference values for an athletic population. (**B**) Value of the mean vectors of U12–15 athletes plotted on the R–Xc graph, proposed in this study as a reference for the U17 and U19 basketball players. F = 60.16, \(p\) < 0.001; U19 vs. <U19: contrast = 0.867 (0.55; 1.18), F = 30.09, \(p\) < 0.001]. Figure 3B shows all slopes plotted in a single scatter graph. Figure 4A shows the relationship between the first component resulting from the PCA and the somatotype components. The first component explains 80.74% of the variance (eigenvalue = 2.42), with a standard error of 0.023 and a measurement bias of 0.004. The coefficients were \(-\)0.614 (CI:\(-\)0.65, \(-\)0.58; z = \(-\)37.86, \(p\) < 0.001), 0.568 (CI: 0.51, 0.63), z = 19.58, \(p\) < 0.001) and 0.547 (CI: 0.48, 0.61; z = 16.54, \(p\) < 0.001) for ecto, meso and endomorphy, respectively, and the PCA component can be estimated as \(-\)1.66204 + (0.648 \(\times\) endomorphy) + (0.5127 \(\times\) mesomorphy) \(-\) (0.493 \(\times\) ectomorphy). In addition, PCA can be predicted by each component separately as follows:  
(a) \(-\)3.6368 + 1.5837 \(\times\) endomorphy (R\({}^{2}\) = 0.723, RMSE = 0.819);  
(b) \(-\)4.842 + 1.2385 \(\times\) mesomorphy (R\({}^{2}\) = 0.784, RMSE = 0.722);  
(c) 4.4294 \(-\) 1.1889 \(\times\) ectomorphy (R\({}^{2}\) = 0.915, RMSE = 0.454). Figure 4B represents the relationship between the PCA component and BIA parameters, where the PCA component increases with PhA and decreases with both R and Xc, linearly. Due to BIA parameters' logarithmic scaling, each increment of 1% in \(\Omega\) corresponds to a unit incrementation in PCA. However, when adjusted by age, the PCA component showed significant results only with Xc [\(\beta\) = \(-\)10.961 (\(-\)20.97, \(-\)0.96), t = \(-\)2.17, \(p\) = 0.032] and PhA [\(\beta\) = 11.39 (1.22, 21.56), t = \(-\)2.21, \(p\) = 0.028]. Figure 3: (**A**) Correlation of Phase Angle (PhA) and mesomorphy adjusted for age categories extracted from the conditional effects of a regression model, with PhA as response and age group and mesomorphy as interaction; (**B**) age slopes from a regression model with PhA as response and mesomorphy as a regressor.