Patterson Jack, Rayner Russell, Carey David L, O'Grady Mathew, Talpey Scott W
Sports Performance and Nutrition Research Group, La Trobe University, Melbourne, Victoria, Australia.
School of Allied Health, Exercise and Sport Science, Charles Sturt University, Port Macquarie, New South Wales, Australia.
J Hum Kinet. 2025 Apr 30;96(Spec Issue):177-186. doi: 10.5114/jhk/202812. eCollection 2025 Feb.
This study aimed to investigate the influence of contextual variables related to team performance, individual performance and scheduling on the external training load placed upon professional basketball players following a won compared to a lost game. Fifteen male professional basketball players from a single club competing in the Australia's top tier National Basketball League (NBL) during the 2023/2024 season participated in this study. Total player load, peak player load, player load per minute and the work to rest ratio derived from accelerometry were measures of external player load used in the analysis. Linear mixed models with the match outcome (win/loss), expected margin vs. outcome, days between games, and player efficiency as fixed effects, and player ID as a random intercept were employed. A statistically significant (p = 0.001) 62.46 au difference in total player load was observed following a win compared to a loss. However, when considering the random effects of an individual, individual performance, team performance and scheduling as fixed effects, a non-significant (p = 0.086) difference was observed with the individual player being the most influential variable. There were no statistically significant differences in peak player load (p = 0.734), player load per minute (p = 0.281), and the work to rest ratio (p = 0.782) following a win compared to a loss. The external training load prescribed to professional basketball players is highly individualized. Practitioners monitoring the training demands of players should consider the influence of individual factors when designing training.
本研究旨在调查与团队表现、个人表现和赛程安排相关的情境变量，对职业篮球运动员在比赛获胜与失利后所承受的外部训练负荷的影响。来自一家俱乐部的15名男性职业篮球运动员参与了本研究，该俱乐部在2023/2024赛季参加澳大利亚顶级国家篮球联赛（NBL）。通过加速度计得出的总运动员负荷、峰值运动员负荷、每分钟运动员负荷以及工作与休息比率，是分析中使用的外部运动员负荷测量指标。采用线性混合模型，将比赛结果（胜/负）、预期分差与实际分差、比赛间隔天数以及运动员效率作为固定效应，将运动员ID作为随机截距。结果发现，与失利相比，获胜后总运动员负荷存在统计学显著差异（p = 0.001），差值为62.46au。然而，当将个体的随机效应、个体表现、团队表现和赛程安排作为固定效应考虑时，观察到差异不显著（p = 0.086），其中个体运动员是最具影响力的变量。与失利相比，获胜后峰值运动员负荷（p = 0.734）、每分钟运动员负荷（p = 0.281）和工作与休息比率（p = 0.782）均无统计学显著差异。职业篮球运动员的外部训练负荷高度个体化。监测运动员训练需求的从业者在设计训练时应考虑个体因素的影响。
