(10) THE RELATIONSHIP OF PLAYERLOAD AND PLAYER EFFICIENCY IN WOMEN COLLEGIATE BASKETBALL ATHLETES

Purpose: Basketball is an intense, intermittent team sport that requires repeated sprints, jumps, accelerations, and changes of direction. During the competitive season, collegiate women basketball (WBB) athletes participate in ~ 30 games and 50 practices, excluding post season play. The consistent exposure to high workloads necessitates athlete load monitoring to quantify movement demands and assess how player efficiency (PE) is affected. The purpose was to assess the relationships between athlete external load and PE across a competitive season. Methods: NCAA D-I WBB athletes ((n=7; guard n=4, forward n=2, center n=1) mean±SD; age 20.57±1.13 yr, height 175.57±7.21 cm, weight 70.94±10.36 kg, body fat% 23.13±3.65) wore microsensors for all practices and games across one season. EL was quantified by Playerload™ (PL), a volume-based proprietary metric. PE was calculated using the formula: (rebounds + assists + steals + blocks) – (missed field goals + missed free throws + turnovers). Athletes included in the study were limited to those who possessed a top 5 PE on their team for at least 70% of all games. A location scale model evaluated the relationship between timepoint, position, PE, previous day PL, and game PL. Game PL was set as the dependent variable and the predictors in the model included: timepoint, previous day PL, PE, and position. Repeated measures correlations assessed the relationship between EL and PE (p< 0.05). Correlation coefficients (r) were classified: small (0.10-0.30), moderate (0.31-0.50), large (0.51-0.70), very large (0.71-0.90), and extremely large (0.91-1.00). Results: The average PL for game 1 for all athletes was 551 ± 113 arbitrary units (AU). PL remained consistent across the season (B=0.20 [95% CI: -0.80, 1.10]).  Previous day practice PL was associated with higher game day PL (B=3.00 [95% CI: 0.50, 5.50]). Athletes with higher PE experienced higher PL; for each 1 unit increase in PE, PL increased by 7.10 AU [95% CI: 0.70, 13.70]. Individual athlete PE remained consistent across the season (B=0.30 [95% CI: 0.00, 0.50], but PE varied across position groups (B = 5.20 [95% CI: 0.20, 16.30]). The average within person variability of PL was 25.50 [95% CI: 23.30, 27.80]. Guards experienced higher within person variability of PL (B=3.30 [95% CI: 0.20, 6.30]). A moderate relationship existed between PL and PE (0.31, p< 0.001). Conclusions: Collegiate WBB athletes experience high within person variability of PL, despite playing on the same team. Previous day PL positively influenced next day PL, higher PL values were associated with increases in PE, and guards experienced higher within person variability of PL. Practical Applications: Strategic management of individual in-season workloads may positively influence individual athlete PE. Variation in individual athlete PE may occur due to positional demands and contextual factors such as tactical game strategy, opposition ranking, and individual playing style. Acknowledgements: None