(16) IN-SEASON WORKLOADS BY SESSION TYPE AND PLAYING STATUS IN A COLLEGIATE WOMEN’S BASKETBALL TEAM

Background: Monitoring athlete workloads in-season enables the quantification of training and competition demands, which may be used to inform decisions relative to balancing physical stress and recovery. However, limited research exists exploring workloads across a collegiate women’s basketball season.

Purpose: To study workload metrics of a women’s collegiate basketball team during practices, pregame sessions, shootarounds, and games, over a competitive season.

Methods: National Collegiate Athletic Association Division I women’s basketball athletes (n=14) participated. Athletes were equipped with wearable 10 Hz tri-axial accelerometers for all practices, pregrame sessions, shootarounds, and games. Workload metrics included: PlayerLoad (PL, AU), player load per minute (PL/min, AU/min), explosive efforts (EE, #), total jumps (TJ, #), high accelerations ( >3.5 m/s², #), and high decelerations (< -3.5 m/s², #). Accumulated game day (GameDay_Acc) workloads were calculated by summing pregame session, shootaround, and game metrics. High-minute players were classified as those who played ≥15 minutes per game (n=8); others were classified as low-minute players (n=6). Separate multivariate analysis of variance (MANOVA) assessed differences in session workloads (practice, pregame session, shootaround, GameDay_Acc) in high and low minute players (p< 0.05). η² effect sizes were determined and classified as: η²=0.01, small effect; η²=0.06, medium effect; and η²=0.14, large effect.

Results: GameDay_Acc workloads were significantly higher for high-minute players (PL: 958 ± 296 (95% CI: 910-1008); EE: 58 ± 24 (95% CI: 54-62); TJ: 126 ± 44 (95% CI: 119-134); high decelerations:12 ± 6 (95% CI: 11-13) than low-minute players (PL: 717 ± 215 AU (95% CI: 642-792 AU); EE: 49 ± 30 (95% CI: 52-56); TJ: 88 ± 41 (95% CI: 78-97); high decelerations: 8 ± 6 (95% CI: 7-10) (p< 0.01, η²=0.03-0.15). Table 1 includes workload metrics of high- and low-minute players for all session types. High-minute players had a greater PL and PL/min, but fewer jumps in games when compared to practices. No differences existed between games and practices for EE, high accelerations, or high decelerations (p >0.05). For low-minute players, all workload metrics were higher in practices than games (p< 0.01).Despite high-minute players encountering higher workloads during games compared to low-minute players (p< 0.001, η²=0.25-0.60), they were also exposed to higher workloads during practices compared to low-minute players (PL:< 0.001; PL/min: p< 0.001; EE: p< 0.001; high accelerations: p=0.007), high decelerations: p=0.030).

Conclusions: High-minute players had higher GameDay_Acc, game, shoot-around, pregame session, and practice loads compared to low-minute players. PRACTICAL APPLICATIONS: High-minute players should receive adequate recovery, while low-minute players receive additional exposures to game-load stresses to ensure they are maintaining appropriate fitness levels for game scenarios.


Acknowledgements: None