## 3 Results
In addition, when the correlations were computed for the elite and non-elite level groups separately, none of the variables in the elite group correlated positively with swimming performance, and in the non-elite group only swimming training and arm span showed direct relationships with performance (r = 0.630, \(p\) < 0.001 and r = 0.453, \(p\) = 0.014, respectively). The artificial neural network model predicted average scores of 515 \(\pm\) 161, 519 \(\pm\) 155, and 516 \(\pm\) 159 WA points (based on the coefficients of determination; see Figure 4), while the true average scores were 515 \(\pm\) 161, 560 \(\pm\) 145, and 529 \(\pm\) 158 WA points for the training, test, and all sets, respectively (cf. Figure 4). In Figure 5, the relative importance of each variable to swimming performance (global impact on the model) is presented, where the hours of swimming training appear as the most important variable, followed by the countermovement jump impulse and fat mass percentage (95, 37, and 21 Shapley additive explanation values, respectively). In contrast, stroke length, flight time, and waist-to-hip ratio only showed marginal global impacts in explaining the swimming performance, but were important for the model construction (0.24, 0.03, and 0.02 Shapley additive explanation values, respectively). The directions (positive or negative) of the variables' impact on swimming performance are presented in Figure 6. Swimming training and impulse contributed positively to swimming performance, where more hours of swimming training and impulse were associated with higher WA points. In contrast, fat mass contributed negatively to swimming performance. In Figure 7, the importance of variables (global impact on the models) was altered when the model was exposed only to elite or non-elite swimmers separately. The impulse went to the first place of importance in both groups' models, and swimming training duration went to the eighth and seventh place in elite and non-elite groups, respectively. In the elite group, the duration of dryland training presented a larger global impact, capable of better explaining the changes in competitive swimming performance than the swimming training duration. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variables** & **Pearson r (WA Points)** & **95\% CI** & **\(p\)-Value** \\ \hline Age (years) & 0.512 & (0.208–0.840) & \textless{}0.001 \\ Training background (years) & 0.482 & (0.237–0.690) & 0.001 \\ Swimming training (h- week\({}^{-1}\)) & 0.920 & (0.883–0.952) & \textless{}0.001 \\ Dryland training (h- week\({}^{-1}\)) & 0.695 & (0.583–0.842) & \textless{}0.001 \\ Body mass (kg) & 0.232 & (\(-\)0.032–0.458) & 0.122 \\ Height (cm) & 0.429 & (0.169–0.643) & 0.003 \\ Body mass index (kg \(\cdot\) m\({}^{2-1}\)) & \(-\)0.025 & (\(-\)0.302–0.262) & 0.871 \\ Hand length (cm) & 0.435 & (0.223–0.676) & 0.003 \\ Arm span (cm) & 0.620 & (0.459–0.749) & \textless{}0.001 \\ Waist/hip ratio & 0.519 & (0.294–0.740) & \textless{}0.001 \\ Fat mass (\%) & \(-\)0.614 & (\(-\)0.728—0.495) & \textless{}0.001 \\ Flight time (s) & 0.714 & (0.545–0.837) & \textless{}0.001 \\ Impulse (N-s) & 0.625 & (0.438–0.767) & \textless{}0.001 \\ Stroke rate (cycle- min\({}^{-1}\)) & 0.695 & (0.240–0.651) & 0.001 \\ Stroke length (m- cycle\({}^{-1}\)) & 0.695 & (0.174–0.595) & 0.005 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Relationships between swimming performance and age, training background, anthropometry, countermovement jump, stroke rate, and stroke length. Figure 5: Hierarchical average global impact on swimming performance model expressed by Shapley additive explanation (SHAP) values. Figure 6: Beeswarm plot illustrating the impact of each of the 16 input variables values on model output, ordered by feature importance. The colour bar on the right is used to interpret the direction of the relationship between an individual variable and competitive swimming performance, with red (highest values) indicating the direction.