## 3 Results
Table 7 presents a principal component analysis (PCA) conducted to reduce the dimensionality of the dataset and identify underlying structures in player performance. Prior to extraction, data suitability was verified using the Kaiser-Meyer-Olkin (KMO) test, which yielded a value of 0.722, and [PERSON]'s test of sphericity, which was significant (\(p\) < 0.001), confirming the adequacy of the data for PCA. Components with eigenvalues greater than 0.9 were retained, and the selection was further supported by the cumulative variance explained (above 82.1%). Factor loadings above 0.30 were used as the threshold for inclusion. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Component**} & \multicolumn{1}{c}{**Unicity**} \\ \cline{2-7}  & **1** & **2** & **3** & **4** & **5** & **6** & **7** & \\ \hline TCi/MIN & 0.924 & & 0.358 & & & & 0.0930 \\ P/MIN & 0.847 & & 0.303 & & & & 0.0137 \\ Tca/MIN & 0.791 & & & 0.302 & & & 0.0293 \\ T2 Pi/MIN & 0.790 & & & & & & 0.0883 \\ Tli/MIN & 0.749 & & & & & 0.347 & 0.1542 \\ T2 Pa/MIN & 0.730 & & & 0.307 & & & 0.0417 \\ Tla/MIN & 0.705 & & & & & & 0.533 & 0.0821 \\ RT/MIN & & 0.928 & & & & & 0.0776 \\ RD/MIN & & 0.859 & & & & & 0.1946 \\ RO/MIN & & 0.763 & & & & & 0.2739 \\ TP/MIN & & 0.719 & & & & & 0.4370 \\ T3 Pa/MIN & & & 0.965 & & & & 0.0570 \\ T3 Pi/MIN & & & 0.896 & & & & 0.1323 \\ \%T3P & & & 0.693 & & & & 0.4265 \\ \%T2P & & & & 0.999 & & & 0.1676 \\ \%TC & & & & 0.855 & & & 0.0806 \\ BR/MIN & & 0.344 & & & 0.883 & & 0.3228 \\ BP/MIN & 0.346 & & & & 0.656 & & 0.3626 \\ AS/MIN & & \(-\)0.366 & & & 0.549 & \(-\)0.425 & & 0.2478 \\ FP/MIN & & & & & & 0.956 & 0.1900 \\ \%TL & & & & & & 0.834 & 0.2881 \\ \hline \hline \end{tabular} Note.: Results of the principal component analysis (PCA), highlighting the most significant variables.  
\end{table} 
Table 7: Principal component analysis (PCA). In this analysis, principal components are identified as linear combinations of the original variables, explaining a significant percentage of the total variance. The eigenvalues and percentages of variance explained indicate the relative importance of each component. For example, the first component explains 34.6% of the variance, and the second and third components explain 16.4% and 9.7%, respectively. These principal components simplified the complexity of the data, enabling a better interpretation and understanding of player performance in women's basketball, particularly in the Women's Euroleague. Figure 1 shows the clustering results in two dimensions. However, the optimal number of clusters (k = 3) was not determined from the visual representation but through statistical methods applied to the entire dataset. Specifically, the gap statistic was computed using the complete set of performance variables, not just the first two principal components. This approach ensures that the number of clusters reflects the full multidimensional structure of player performance. The use of a 2D cluster plot is intended solely for visual interpretation, where Dim 1 and Dim 2 serve as a projection of the original dataset, capturing over 50% of the total variance. Therefore, the three-cluster solution visualized in the figure is based on the full dataset and is not limited by the reduced dimensionality of the plot. These clusters represent distinct roles that players fulfill in women's basketball beyond the traditional positions. Each cluster groups players based on similarities in their performance statistics, enabling a new classification rooted in their specific contributions to the game. Table 8 shows the distribution of players within each cluster proposed by the k-means clustering analysis. This distribution provides the basis for evaluating the significant differences among the groups identified. \begin{table}
\begin{tabular}{c c} \hline \hline
**Cluster No** & **Count** \\ \hline
1 & 134 \\
2 & 118 \\
3 & 94 \\ \hline \hline \end{tabular} _Note._ Distribution of players across clusters based on the roles identified through k-means clustering analysis.  
\end{table}