## 3 Results
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Team** & **Test 1** & **Test 2** & **Test 3** & **Test 4** & **Test 5** & **Test 6** \\ \hline U12 & \begin{tabular}{c} \(15.8\pm 4.6\) \\ (n = 22) \\ \end{tabular} & \(10.9\pm 2.3\) (n = 24) & \(15.7\pm 4.9\) (n = 21) & \(9.26\pm 2.0\) (n = 24) & \(22.6\pm 5.0\) (n = 24) & \(15.7\pm 4.9\) \\ U13 & \begin{tabular}{c} \(21.7\pm 2.6\) \\ (n = 22) \\ \end{tabular} & \(11.0\pm 2.0\) (n = 22) & \(20.6\pm 3.0\) (n = 22) & \(9.05\pm 2.4\) (n = 22) & \(23.4\pm 5.4\) (n = 21) & \(20.6\pm 3.0\) (n = 22) \\ \hline U14 & \begin{tabular}{c} \(22.4\pm 3.1\)\({}^{*}\) \\ (n = 22) \\ \end{tabular} & \(11.6\pm 2.3\) (n = 22) & \(19.3\pm 3.2\) (n = 21) & \(10.7\pm 2.2\) (n = 22) & \(27.7\pm 3.8\)\({}^{\rm ab}\) & \(19.3\pm 3.2\) (n = 24) & \((n=21)\) \\ \hline U15 & \begin{tabular}{c} \(24.3\pm 2.2\)\({}^{*}\) \\ (n = 24) \\ \end{tabular} & \(12.7\pm 2.3\) (n = 24) & \(18.7\pm 3.5\) (n = 24) & \(11.5\pm 2.3\)\({}^{\rm ab}\) & \(28.4\pm 2.3\)\({}^{\rm ab}\) & \(18.7\pm 3.5\) (n = 24) & \((n=24)\) \\ \hline U16 & \
\begin{tabular}{c} \(25.0\pm 3.8\)\({}^{\rm ab}\) \\ (n = 19) \\ \end{tabular} & \(12.9\pm 2.1\) (n = 19) & \(20.2\pm 5.5\)\({}^{\rm a}\) (n = 19) & \(11.3\pm 3.2\)\({}^{\rm b}\) (n = 19) & \(
\begin{tabular}{c} \(30.9\pm 5.1\)\({}^{\rm ab}\) \\ (n = 20)) \\ \end{tabular} & \(20.2\pm 5.5\) (n = 19) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance on passing tests (passes/test) for each age group from the soccer academy. Differences among age groups identified with ANOVA and Tukey multiple comparisons. Post hoc comparisons between ages groups for each test were conducted, and significant differences (\(p\) < 0.05) to U12 are denoted by \({}^{*}\), U13 denoted by \({}^{\rm b}\), U14 denoted by \({}^{\rm c}\), U15 denoted by \({}^{\rm d}\), U16 denoted by \({}^{\rm e}\), U17 denoted by \({}^{\rm f}\). The analyses of residuals of passing performance indicate that test 1 (F = 55.2, \(p\) < 0.001) and test 3 (F = 12.0, \(p\) < 0.001) can discriminate between elite and sub-elite groups (Table 4). On both tests, the elite group's performance was significantly better than both the sub-elite groups (Figure 3). The elite group had significantly higher passing scores than the two sub-elite groups on both tests 1 and 2 (_p_ < 0.001). The average scores for the elite group were 22.84 \(\pm\) 4.60 and 19.58 \(\pm\) 4.08 for tests 1 and 3, respectively. The average scores for sub-elite group A and B for test 1 were 19.73 \(\pm\) 2.71 and 19.80 \(\pm\) 2.32, respectively. The passing scores for sub-elite group A and B for test 3 were 16.58 \(\pm\) 3.09 and 18.93 \(\pm\) 2.55, respectively. \begin{table}
\begin{tabular}{l c c c} \hline  & **Intercept** & \(\beta\) & **R\({}^{2}\)** \\ \hline Test 1 & \(-\)174.17 * & 48.39 ** & 0.35 \\ Test 3 & 106.14 & 19.79 ** & 0.10 \\ \hline \end{tabular} \({}^{*}\)_p_ < 0.05, ** 0.001.  
\end{table} 
Table 4: Effect of age on passing test 1 and 3. Each pairing of intercept and \(\beta\) represents a separate linear model. Only data from the elite academy were included in these models and data from both tests were square transformed. Figure 3: Comparison of passing performances in two passing tests among three soccer clubs—one elite (black dots) and two sub-elite academies (red and green dots). Panel (**A**) and panel (**B**) show the relationship between age and performance on test 1 and test 3, respectively. Regression lines in panel (**A**) and panel (**B**) were estimated using data from the elite academy only. Panel (**C**) and panel (**D**) show the residuals calculated from panel (**A**) and panel (**B**), respectively. Solid black lines in panel (**C**) and panel (**D**) indicate group means.