Game-related statistics.

This study was designed to support the tactical decisions of wheelchair basketball (WB) coaches in identifying the best players to form winning lineups. Data related to a complete regular season of a top-level WB Championship were examined. By analyzing game-related statistics from the first round, two clusters were identified that accounted for approximately 35% of the total variance. Cluster 1 was composed of low-performing athletes, while Cluster 2 was composed of high-performing athletes. Based on data related to the second round of the Championship, we conducted a two-fold evaluation of the clusters identified in the first round with the team’s net performance as the outcome variable. The results showed that teams where players belonging to Cluster 2 had played more time during the second round of the championship were also those with the better team performance (R-squared = 0.48, p = 0.035), while increasing the playing time for players from Classes III and IV does not necessarily improve team performance (r2 = -0.14, p = 0.59). These results of the present study suggest that a collaborative approach between coaches and data scientists would significantly advance this Paralympic sport.本研究旨在辅助轮椅篮球（Wheelchair Basketball, WB）教练制定战术决策，以遴选最优球员组建制胜阵容。本研究对某顶级轮椅篮球锦标赛完整常规赛的相关数据进行了分析。通过对首轮赛事的比赛相关统计数据进行剖析，共识别出两个聚类，其方差解释占比约为总方差的35%。聚类1由表现欠佳的运动员构成，聚类2则由表现优异的运动员组成。基于该锦标赛第二轮赛事的相关数据，本研究以球队净表现作为因变量，对首轮识别出的聚类开展了双重评估。结果显示，在锦标赛第二轮赛事中，聚类2球员出场时间更多的球队，其团队表现也更为出色（决定系数R²=0.48，p=0.035）；而提升III级与IV级球员的出场时间，未必能改善团队表现（r²=-0.14，p=0.59）。本研究的上述结果表明，教练与数据科学家之间的协同合作，将显著推动该残奥会项目的发展。