## 3 Results  
The birthdate distribution of all young players (n = 285) was not statistically different from the expected distribution for each quartile (\(p>0.05\), Table 1).  
There is no relationship between the quartile and the ranking in boys (\(\chi^{2}\) = 5.488; df = 3; \(p=0.139\); Vc = 0.182) or in girls (\(\chi^{2}\) = 5.839; df = 3; \(p=0.120\); Vc = 0.221) (Table 2). In boys, there is a higher proportion of players born in Q1 (CSR = 2.2).  
There is no relationship between the quartile and the ranking in boys (F = 6.842; df = 3; contingency coefficient = 0.207; \(p=0.056\), \(I\)C\({}_{\rm MonteCarlo}\) = [0.052, 0.056]) or in girls (F = 5.060; df = 3; contingency coefficient = 0.205; \(p=0.112\), \(I\)C\({}_{\rm MonteCarlo}\) = [0.106, 0.118]) (Table 3). However, in boys, there is a higher proportion of players born in the last year possible (CSR = 2.0), and in girls, there is a higher proportion of players born in the third last year (CSR = 2.1).  
\begin{table}
\begin{tabular}{l l c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Number and (\%) of Young Athletes Per Quantile**} & \multicolumn{3}{c}{\(\mathbf{\chi^{2}}\)} & \(\boldsymbol{p}\) \\  & **Q1** & **Q2** & **Q3** & **Q4** & **Total** & & \\ \hline All athletes & 70 (24.6) & 79 (27.7) & 78 (27.4) & 58 (20.4) & 285 & 3.968 & 0.265 \\ By sex & & & & & & & \\ Male & 35 (21.2) & 44 (26.7) & 48 (29.1) & 38 (23.0) & 165 & 2.491 & 0.477 \\ Female & 35 (29.2) & 35 (29.2) & 30 (25.0) & 20 (16.7) & 120 & 5.000 & 0.172 \\ Boys by country & & & & & & & \\ Position & Country & & & & & & \\
1 & Spain & 5 (41.7) & 2 (16.7) & 0 (0.0) & 5 (41.7) & 12 & 1.500 & 0.622 \\
2 & France & 3 (30.0) & 5 (50.0) & 1 (10.0) & 1 (10.0) & 10 & 4.400 & 0.261 \\
3 & Sweden & 1 (8.3) & 4 (33.3) & 7 (58.3) & 0 (0.0) & 12 & 4.500 & 0.115 \\  
weden & 2 (16.7) & 2 (16.7) & 7 (58.3) & 1 (8.3) & 12 & 7.333 & 0.065 \\
7 & The Netherlands & 4 (40.0) & 3 (30.0) & 0 (0.0) & 3 (30.0) & 10 & 0.200 & 1.000 \\
8 & Estonia & 3 (42.9) & 2 (28.6) & 1 (14.3) & 1 (14.3) & 7 & 1.571 & 0.846 \\
9 & Finland & 2 (25.0) & 3 (37.5) & 2 (25.0) & 1 (12.5) & 8 & 1.000 & 0.962 \\
10 & Hungary & 0 (0.0) & 2 (20.0) & 6 (60.0) & 2 (20.0) & 10 & 3.200 & 0.242 \\
11 & Germany & 2 (16.7) & 3 (25.0) & 5 (41.7) & 2 (16.7) & 12 & 2.000 & 0.705 \\
12 & Czech Republic & 2 (22.2) & 2 (22.2) & 1 (11.1) & 4 (44.4) & 9 & 2.111 & 0.654 \\ \hline \hline \end{tabular} Note.* _p \(<\) 0.05._  
\end{table}
Table 2: Birth quartile distribution between top- and bottom-ranked boys’ and girls’ teams.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Boys**} & \multicolumn{3}{c}{**Rank 1–8**} & \multicolumn{3}{c}{**Rank 9–16**} \\  & **n** & **\%** & **CSR** & **n** & **\%** & **CSR** \\ \hline Q1 & 24 & 27.9 a  & 2.2 & 11 & 13.9 b  & \(-\)2.2 \\ Q2 & 23 & 26.7 & 0.0 & 21 & 26.6 & 0.0 \\ Q3 & 21 & 24.4 & \(-\)1.4 & 27 & 34.2 & 1.4 \\ Q4 & 18 & 20.9 & \(-\)0.7 & 20 & 25.3 & 0.7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Evaluation of birth quartiles of young padel athletes participating in the European Junior Championships by Teams 2024 by sex, using [PERSON]\({}^{2}\) tests, and by country, using [PERSON]’s exact tests.