## 2 Methods
### Statistical Analyses  
We used principal component analysis (PCA) to characterize patterns of variation among player's height, mass and age (birth date was converted to decimal age) creating a multivariate age and size index (ASI) [14]. All variables were first standardized to a mean of zero and a standard deviation of one. The first component (PC\({}_{\text{ASI}}\)) accounted for 89.4% of the variance in the data (Supplementary Table S1) and all vectors loaded in the same direction with larger positive values indicating greater age and size. Estimates of repeatability for each test were calculated using intra-class correlation coefficients (two-way mixed effects model) with the \"psych\" package of R. For summary statistics, a [PERSON]'s ANOVA followed by Games-Howell post hoc test [22] detected differences in performance among the passing tests, and paired correlations were estimated with the \"Hmisc\" package of R [23]. To confirm that each passing test could discriminate among different age groups, we ran separate ANOVA's followed by Tukey Honest Significant Difference (95% confidence intervals) [24] to detect differences in performance among teams. Prior to correlation and ANOVA analyses, data from tests one, two and three were corrected with a square transformation to achieve normality. All tests were then included in a PCA to characterize patterns of variation among the correlated measures of passing performance. Prior to PCA, data from each passing test were standardized to a mean of zero and a standard deviation of one. The first component of the PCA (PC\({}_{\text{P1}}\)) explained 56.1%  
Figure 1: Graphical representation of the six passing tests used in this study. of the variation observed in the data (Supplementary Table S2); because all vectors of PC\({}_{\rm P1}\) loaded in the same direction, and larger positive values were indicative of higher passing performance over all tests, PC\({}_{\rm P1}\) represents overall passing and control performance. The second component of the PCA (PC\({}_{\rm P2}\)) explained 11.2% of the variation. To determine the independent effects of age, height, mass and ASI on performance, we ran separate linear models [24] for each of these variables, estimating their effect on overall passing and control performance (PC\({}_{\rm P1}\)). Passing performance (PC\({}_{\rm P1}\)) was first corrected with a square transformation to achieve normality. To evaluate the passing tests' construct validity, we compared the performance of athletes from the elite Brazilian academy with athletes from two lower ranked academies from Brazil, referred to subsequently as sub-elite A and sub-elite B. Both sub-elite clubs completed test 1 and test 3 as described earlier, and the age of players were recorded on the day of testing--sub-elite A (n = 51, 12.96 \(\pm\) 1.30, range = 10.61-15.10 years) and sub-elite B academy (n = 51, 16.35 \(\pm\) 1.97, range = 12.69-19.79 years). To compare performance among the academies, we needed to account for the varying distribution of ages among them. To achieve this, we first ran separate linear models estimating the effect of age on test 1 performance (square transformed) and test 3 performance (square transformed), using only data from the elite academy. We then used parameter estimates from these models to calculate residuals, separately for each test, for players from all three academies. These residuals indicate the difference between actual player performance and the performance that is expected for their age. Then, an ANOVA followed by Tukey Honest Significant Difference (95% confidence intervals) identified differences among the academies on residual performance, separately for each test. Normality of residuals used in analyses was assessed using the Shapiro-Wilk test and visual inspections of Q-Q plots using R. We used power analyses to determine if we had sufficient statistical power to detect significant effects for both continuous traits (regression) and among groups (ANOVA) [24].