Characterization of Workloads Across Three Seasons in Elite Division I Collegiate Women’s Basketball Players

Abstract

Hudy, A, Shah, J, Kraemer, WJ, Burland, JP, Glaviano, NR, Morgan, K, Wilson, J, Casa, DJ, and Huggins, RA. Characterization of workloads across three seasons in elite Division I collegiate women’s basketball players. J Strength Cond Res XX(X): 000–000, 2026 -This paper aims to characterize and compare external workloads of elite collegiate NCAA Division I women's basketball players across multiple seasons and within the 5 phases of each season. Monitoring workloads can facilitate periodization for optimal performance, reduce injury risk, and guide return-to-play protocols. Three seasons were analyzed. Each season included the following phases: 8-hour preseason (P08), 20-hour preseason (P20), nonconference play (NON), conference play (CON), and championship play. The variables monitored were PlayerLoad (PL), PlayerLoad·min ⁻¹ (PL/min), Explosive Efforts, Total Jumps, Hi Inertial Movement Analysis (IMA) Accelerations, and Hi IMA Decelerations (HD). Fourteen women (ages 21 ± 2 years), categorized as high-impact players (average ≥ 15 min/game), wore triaxial accelerometers during each individual, practice, and gameday activity for 3 consecutive seasons. A 3 × 5 repeated measures analysis of variance was conducted for each season's team of 7 high-impact players ( n = 7). Post hoc analysis was performed to identify the significant ( p < 0.05) interactions between the seasons and phases. The results show that across 3 NCAA Division I women's basketball seasons, the 20-hour preseason (P20) consistently exhibited the highest external loads, averaging peak PL values and significantly elevated high-intensity movements such as accelerations and decelerations, underscoring the preparatory demands of this mesocycle. Although external loads varied significantly between phases, unexpected between-year differences emerged within the same phases, particularly in PL·min ⁻¹ , highlighting the influence of contextual variables like athlete availability, training structure, and informal preseason play. Future research can explore the variations of workload demands within each phase to better understand metrics and models that can be used to optimize player performance.