## 3 Results  
The regional distribution of participants at the 2023 RG WCH revealed that the majority (54.1%) originated from Europe, followed by 27.7% from Asia and Oceania, 13.5% from the Americas, and 4.7% from Africa. Figure 1 presents the frequency distribution of athletes' birthdates based on their respective geographical regions. Results from the \(\chi^{2}\) goodness-of-fit tests indicated no statistically significant differences in birth quartile distributions across any of the regions: Africa (\(\chi^{2}\)[3, 27] = 1.296, \(p>0.05\), \(w=0.22\)), the Americas (\(\chi^{2}\)[3, 78] = 7.538, \(p>0.05\), \(w=0.31\)), Asia and Oceania (\(\chi^{2}\)[3, 160] = 3.35, \(p>0.05\), \(w=0.14\)), and Europe (\(\chi^{2}\)[3, 313] = 4.482, \(p>0.05\), \(w=0.12\)). Figure 1: Frequency distribution of birthdates among 2023 RG WCH participants by region of origin. The frequency and percentage distributions of birthdates among junior and senior RGs competing in both individual and group events, along with the results of the \(\chi^{2}\) goodness-of-fit analyses, are presented in Table 1. The analysis revealed a statistically significant deviation from the expected uniform distribution in the Group gymnast sample, accompanied by a small effect size (\(\chi^{2}[3,349]=10.186\), \(p<0.05\), \(w=0.17\)). Post hoc analysis showed a significant under-representation of athletes born in the fourth quartile (Q4; SR \(=-2.17\)). Similarly, the total junior sample demonstrated a significant under-representation of gymnasts born in Q4 (SR \(=-2.54\)), also with a small effect size (\(\chi^{2}[3,356]=9.833\), \(p<0.05\), \(w=0.17\)). Within the junior category, 15-year-old gymnasts showed a significant departure from the expected distribution (\(\chi^{2}[3,183]=10.027\), \(p<0.05\), \(w=0.23\)), marked by an atypical over-representation of those born in Q3 (SR \(=2.55\)). No statistically significant differences in birthdate distribution were found for the entire sample of elite RGs, the overall individual category, junior individuals, junior groups, or any of the senior subgroups (\(p>0.05\)). Some comparisons yielded non-significant results, potentially due to limitations in sample size affecting the findings. The distribution frequencies and percentages of birthdates for finalists and N-Fs, along with corresponding \(\chi^{2}\) test results, are provided in Table 2. The overall finalists group showed a significant deviation from the expected distribution, with a small effect size (\(\chi^{2}[3,167]=11.563\), \(p<0.05\), \(w=0.26\)). Adjusted residuals indicated a significant over-representation of athletes born in Q3 (SR \(=2.51\)) and an under-representation of those born in Q4 (SR \(=-2.28\)). In the group finalists sample, a similar pattern emerged: RGs born in Q3 were significantly over-represented (SR \(=2.71\)), while those born in Q4 were under-represented (SR \(=-2.44\)), with a medium effect size (\(\chi^{2}[3,127]=13.315\), \(p<0.05\), \(w=0.32\)). The junior total finalists (\(\chi^{2}[3,79]=10.772\), \(p<0.05\), \(w=0.37\)) and junior group finalists (\(\chi^{2}[3,59]=11.305\), \(p<0.05\), \(w=0.44\)) also showed significant deviations from an equal distribution, both with medium effect sizes. In the junior total finalists subgroup, Q4 athletes were under-represented (SR \(=-2.64\)). Among junior group finalists, Q3 gymnasts were over-represented (SR \(=2.41\)), while Q4 gymnasts were under-represented (SR \(=-2.28\)).