Exploring New Dimensions in the Classification of Positions in Women’s Basketball: A Statistical Approach

The aim of this study was to explore new dimensions in the classification of positions in women’s basketball through a comprehensive statistical approach. A total of 386 players from the last three seasons (2021–2024) of the Women’s Euroleague were analyzed based on official performance statistics. Inclusion criteria required players to have participated in all three seasons, with a minimum of 20 min per game across at least five games per season. Using data from the last three seasons of the Women’s Euroleague, analysis of variance, principal component analysis, and k-means clustering were performed to identify specific playing patterns and roles. All performance indicators were normalized per minute to ensure comparability. ANOVA tests revealed significant statistical differences between traditional positions (p < 0.05), validating the relevance of positional analysis. PCA was then used to reduce dimensionality and extract the key performance components, while k-means clustering grouped players according to similar in-game behaviors. The results revealed significant differences between traditional positions (with a significance criterion of p < 0.05) and suggested the need for an updated position classification to better reflect the current dynamics of modern gameplay. According to Euroleague players’ performance, the cluster analysis revealed that three main roles emerged: “perimeter specialists”, “defensive specialists”, and “primary scorers and rebounders”. This reclassification highlights the increasing tactical and statistical complexity of women’s basketball, moving beyond rigid position labels. This new framework can positively influence training and competition strategies. It also provides coaches, analysts, and talent developers with a data-driven tool for roster optimization, role assignment, and game planning in elite-level women’s basketball.