## 4 Discussion  
The primary objective of this study was to identify statistically significant differences across traditional playing positions in women's basketball and to explore new role-based classifications using PCA and clustering. The hypothesis that performance indicators differ significantly between positions and that a new classification model can be developed was confirmed by the results. The five traditional positions (point guard (PG), shooting guard (SG), small forward (SF), power forward (PF), and center (C)) showed considerable variations in their performance across most game statistics analyzed, showing the importance of this classification in the development of basketball performance. [PERSON] et al. [13] conducted a position-specific cluster analysis of basketball players and found that even within the five conventional roles, performance-based subgroups emerge, suggesting that the traditional classification may not fully capture the diversity of player behavior and contribution. Their findings support the use of data-driven models to reveal more accurate groupings based on actual game performance rather than fixed positional labels. Similarly, [PERSON] [31] analyzed the evolution of player roles in the NBA and highlighted how player classifications have become increasingly fluid over time. His work documents how hybrid roles, such as \"point forwards\" or \"stretch pigs\", have emerged, reflecting tactical shifts and the influence of individual versatility. Together, these studies advocate for a dynamic and evidence-based re-evaluation of positional roles, which aligns directly with the objectives of the present research. PGs demonstrated outstanding results in assists per minute and generated a substantial number of points per minute, reflecting their essential role in playmaking and quick scoring in women's basketball [4,32]. SGs showed high efficiency in three-point shooting and points per minute, highlighting their long-range scoring ability [4]. SFs exhibited a balance in defensive rebounds and assists, indicating their versatility in both defense and facilitating plays [4,13]. Power forwards (PFs) were above average in defensive rebounds and were closely matched with centers in blocks per minute, emphasizing their importance  
Figure 4: Significant variables. Cluster 3: paint dominators and primary scorers. This analysis highlights the comparison among the three clusters in key performance metrics, including points (**a**), total rebounds (**b**), two-point shots made (**c**), and offensive rebounds (**d**). in defense and control of the paint [4; 13; 32]. Cs dominated in total rebounds and field goal efficiency, playing a crucial role in scoring near the basket and ball recovery [4; 32]. The advanced statistical analysis conducted in this study unveiled new dimensions in the classification of positions in Women's Euroleague basketball, emphasizing the importance of updating and expanding traditional methodologies for position classification [4; 32]. This aligns closely with [PERSON]'s study, which re-examines men's basketball positions using clustering techniques [33]. Like this research, [PERSON] utilized advanced data analysis techniques to redefine roles in men's basketball, incorporating a greater number of variables and proposing continuous learning to understand the evolving dynamics of roles over time. Cluster 1 identified \"Perimeter Specialists\". These players had a crucial performance in long-distance shooting and their ability to stretch opposing defenses, creating spaces for interior or paint players. Their key attributes were as follows:  
* High frequency and efficiency in three-point shooting: Demonstrated through metrics such as three-point attempts per minute (T3 Pi/MIN) and three-point shooting percentage (%T3P). * Significant contribution in assists: Highlighting their role in facilitating scoring opportunities for themselves and teammates. This role is pivotal in breaking down defenses and creating scoring opportunities, both directly and indirectly.