Despite significant advances in basketball analytics, professional women`s leagues remain underrepresented in performance research. Understanding normative performance benchmarks in professional women`s basketball is essential for informed player development, scouting, and tactical planning; however, this area remains underexplored. This study analysed ten seasons (2012-2022) of performance data from 609 players, Spain`s top-tier women`s league (LF Endesa). Principal Component Analysis (PCA) confirmed the adequacy of 15 key indicators normalized per minute. A two-step clustering approach identified six functional player profiles (Primary Post, Secondary Post, Playmaker, 3&D Specialist, Role Player and Versatile). Players were further stratified into high, mid, and low performers within each role using z-score tertiles. Linear mixed models revealed that High performers consistently outscored Low performers in key metrics such as 2-point and 3-point field goals made, assists, and defensive rebounds (p < 0.01). Convergent validity was supported by the overrepresentation of High performers among First Team selections and players from top-ranked teams. Normative values for each role and performance tier are presented, providing a valuable reference for talent identification and role-based benchmarking in professional women`s basketball. Future research should integrate contextual variables and advanced tracking data to refine these classifications across broader competitive settings.
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