Profiling high performers in elite women’s basketball: Functional roles and normative benchmarks from 10 seasons of Spanish First Division data

Abstract

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.