## 2 Materials and Methods
### Statistical Analysis  
Statistical analysis of the data were conducted using IBM(r) SPSS(r) software, version 28, and Microsoft Excel(r) for Mac, version 16.83. Differences between the relative age quartiles in the studied populations and age groups were evaluated using the Chi-square goodness-of-fit test, comparing them against the actual birth distribution in Spain. For the Chi-square tests, the effect size was calculated following the recommendations of [PERSON] et al. [11]. Specifically, Cramer's V was computed, a commonly used statistic in a similar study [18]. Cramer's V is one of the most frequently used measures to evaluate the strength of association when the Chi-square statistic is significant [66]. Its calculation follows the following formula (Equation (1)):  
\[Cramer^{\prime}s\ V=\sqrt{\frac{\chi^{2}}{N\ (k-1)}} \tag{1}\]  
where \(\chi\)2 is the Chi-square value, N is the sample size, and k is the smaller number of rows or columns in the contingency table. Cramer's V is also known as the effect size (ES). Table 3 shows the reference values for classifying the strength of the association according to degrees of freedom [67]. Additionally, to determine statistically significant differences between quartiles, a Z-test for proportions was applied. The alpha level was set at 0.05. Finally, odds ratios (OR) and their corresponding 95% confidence intervals (95% CI) were calculated for the distribution of relative age quartiles, differentiating between the \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sex** & **Age Groups** & **Q1** & **Q2** & **Q3** & **Q4** & **Total (_n_)** \\ \hline Mixed & U-10 & 114 & 81 & 91 & 72 & 358 \\ Female & F-12 & 189 & 195 & 153 & 153 & 690 \\  & F-14 & 260 & 286 & 205 & 259 & 1010 \\  & F-16 & 221 & 250 & 187 & 221 & 879 \\  & F-18 & 176 & 137 & 124 & 132 & 569 \\  & F-20 & 115 & 101 & 88 & 83 & 387 \\  & F-Elite & 118 & 149 & 123 & 130 & 520 \\ Male & M-12 & 254 & 289 & 235 & 196 & 974 \\  & M-14 & 358 & 378 & 312 & 295 & 1343 \\  & M-16 & 322 & 324 & 300 & 292 & 1238 \\  & M-18 & 228 & 239 & 191 & 182 & 840 \\  & M-20 & 179 & 189 & 162 & 144 & 674 \\  & M-Elite & 273 & 226 & 158 & 226 & 883 \\ \hline \hline \end{tabular} Note. U-10 (\(<\)10 years); M/F-12 (11–12 years); M/F-14 (13–14 years); M/F-16 (15–16 years); M/F-18 (17–18 years); and M/F-20 (19–20 years), where M means “male” and F means “female”. QI = born in the first quarter of the year (January–March); Q2 = born in the second quarter of the year (April–June); Q3 = born in the third quarter of the year (July–September); Q4 = born in the fourth quarter of the year (October–December).  
\end{table} 
Table 2: The total number of participants (_n_) by birth quartile (Q), age groups, and sex. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Degrees of Freedom** & **Small** & **Medium** & **Large** \\ \hline
1 & 0.10 & 0.30 & 0.50 \\
2 & 0.07 & 0.21 & 0.35 \\
3 & 0.06 & 0.17 & 0.29 \\
4 & 0.05 & 0.15 & 0.25 \\
5 & 0.04 & 0.13 & 0.22 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Effect size for the Chi-square test, Cramer’s V, and its interpretation [38]. various samples and considering the sex factor, following the method described by [PERSON] et al. [1]. The OR is interpreted as a comparison between the probabilities of exposure (e.g., in a sports context) and the probabilities of non-exposure (e.g., in the general population). Although Cramer's V was used as the primary measure to assess the strength of association, odds ratios were calculated as a complementary measure to provide additional information on the relative likelihood of belonging to specific birth quartiles. To calculate the OR, we compared the frequency of athletes born in each quartile with the combined frequency of athletes born in the remaining three quartiles within each competitive group.