## 3 Results

Table 8: Player distribution across clusters. Figure 1: Player distribution across clusters. Visualization of the distribution of players in the clusters, identified through k-means clustering analysis. To evaluate the statistical significance and practical magnitude of differences among the three clusters identified via k-means, a one-way Welch ANOVA was conducted for each performance variable. This was followed by Games-Howell post hoc comparisons. Table 9 summarizes the F-values, degrees of freedom, and effect size calculations using eta squared (n\({}^{2}\)), providing a clearer understanding of how strongly each variable contributes to differentiating the clusters. Figure 2 highlights the standout characteristics of players grouped in Cluster 1, referred to as \"Perimeter Specialists\". These players excel in long-distance shooting and their ability to stretch opposing defenses, creating spaces for interior or paint players. Figure 3 highlights the defining characteristics of players grouped in Cluster 2, referred to as \"Defensive Specialists\". Figure 4 highlights the defining characteristics of players grouped in Cluster 3, identified as \"Primary Scorers and Rebounders\". The Games-Howell post hoc comparisons revealed statistically significant differences (\(p<0.001\)) across clusters in a wide range of performance variables. Specifically, Cluster 3 (\"Paint Dominators and Primary Scorers\") consistently outperformed the other two in variables such as points per minute, two-point attempts, two-point makes, and total rebounds. These findings confirm the offensive and interior dominance of this group. Conversely, Cluster 1 (\"Perimeter Specialists\") showed significantly higher values in three-point attempts, three-point makes, and three-point percentage when compared to both Clusters 2 and 3, highlighting their specialization in long-range shooting. Additionally, Cluster 1 also contributed more to assists, reflecting their dual role as shooters and facilitators. Cluster 2 (\"Defensive Specialists\") was characterized by significantly higher values in personal fuels than Cluster 1, suggesting a more physically intense, defense-oriented style of play. Some variables, such as turnovers and steals, did not show significant differences between clusters, indicating that they are not key discriminants of the roles identified. \begin{table}
\begin{tabular}{l l l l l l} \hline  & **F** & **gl1** & **gl2** & \(p\) & \(\mathbf{n^{2}}\) \\ \hline P/MIN & 306.27 & 2 & 200 & \(<\)0.001 & 0.754 \\ T2 Pa/MIN & 172.89 & 2 & 200 & \(<\)0.001 & 0.634 \\ T2 Pi/MIN & 192.38 & 2 & 209 & \(<\)0.001 & 0.648 \\ \%T2P & 25.54 & 2 & 226 & \(<\)0.001 & 0.184 \\ T3 Pa/MIN & 169.61 & 2 & 216 & \(<\)0.001 & 0.611 \\ T3 Pi/MIN & 137.55 & 2 & 211 & \(<\)0.001 & 0.566 \\ \%T3P & 61.43 & 2 & 175 & \(<\)0.001 & 0.412 \\ TCA/MIN & 273.67 & 2 & 200 & \(<\)0.001 & 0.732 \\ Tci/MIN & 167.53 & 2 & 212 & \(<\)0.001 & 0.612 \\ \%TC & 79.54 & 2 & 211 & \(<\)0.001 & 0.43 \\ TLA/MIN & 53.05 & 2 & 208 & \(<\)0.001 & 0.338 \\ TLi/MIN & 68.17 & 2 & 206 & \(<\)0.001 & 0.398 \\ \%TL & 8.56 & 2 & 221 & \(<\)0.001 & 0.072 \\ RO/MIN & 70.53 & 2 & 179 & \(<\)0.001 & 0.441 \\ RD/MIN & 97.14 & 2 & 207 & \(<\)0.001 & 0.484 \\ RT/MIN & 119.62 & 2 & 198 & \(<\)0.001 & 0.547 \\ AS/MIN & 12.28 & 2 & 228 & \(<\)0.001 & 0.097 \\ FP/MIN & 14.47 & 2 & 208 & \(<\)0.001 & 0.122 \\ BP/MIN & 1.32 & 2 & 222 & 0.269 & 0.012 \\ BR/MIN & 1.12 & 2 & 221 & 0.329 & 0.01 \\ TP/MIN & 30.49 & 2 & 199 & \(<\)0.001 & 0.235 \\ \hline \end{tabular} Note. Results of the one-way analysis of variance (ANOVA) to identify significant differences among cluster positions, with a significance criterion of \(p<0.05\).  
\end{table} 
Table 9: One-way clusters ANOVA results. These results support the functional and tactical distinctions captured by the cluster analysis and align with the practical demands of each identified role in elite-level women's basketball. Figure 3: Significant variables. Cluster 2: defensive specialists.