Identifying combinations of players (that is, lineups) in basketball - and other sports - that perform well when they play together is one of the most important tasks in sports analytics. One of the main challenges associated with this task is the frequent substitutions that occur during a game, which results in highly sparse data. In particular, a National Basketball Association (NBA) team will use more than 600 lineups during a season, which translates to an average lineup having seen the court in approximately 25-30 possessions. Inevitably, any statistics that one collects for these lineups are going to be noisy, with low predictive value. Yet, there is no existing work (in the public at least) that addresses this problem. In this work, we propose a regression-based approach that controls for the opposition faced by each lineup, while it also utilizes information about the players making up the lineups. Our experiments show that L-RAPM provides improved predictive power than the currently used baseline, and this improvement increases as the sample size for the lineups gets smaller.

翻译：识别篮球及其他体育项目中表现优异的球员组合（即阵容）是体育分析中最重要的任务之一。与此任务相关的主要挑战之一是比赛中频繁的换人，这导致数据高度稀疏。具体而言，一支美国职业篮球联赛（NBA）球队在一个赛季中会使用超过600套阵容，这意味着平均每套阵容仅经历大约25-30次进攻回合。因此，为这些阵容收集的任何统计数据都不可避免地存在噪声，且预测价值较低。然而，目前（至少公开领域）尚无研究专门解决这一问题。在本工作中，我们提出一种基于回归的方法，该方法既控制了每套阵容所面对的对手实力，又利用了构成阵容的球员信息。我们的实验表明，与当前使用的基线方法相比，L-RAPM提供了更强的预测能力，且随着阵容样本量的减小，这种改进效果更为显著。