NBA team managers and owners try to acquire high-performing players. An important consideration in these decisions is how well the new players will perform in combination with their teammates. Our objective is to identify elite five-person lineups, which we define as those having a positive plus-minus per minute (PMM). Using individual player order statistics, our model can identify an elite lineup even if the five players in the lineup have never played together, which can inform player acquisition decisions, salary negotiations, and real-time coaching decisions. We combine seven classification tools into a unanimous consent classifier (all-or-nothing classifier, or ANC) in which a lineup is predicted to be elite only if all seven classifiers predict it to be elite. In this way, we achieve high positive predictive value (i.e., precision), the likelihood that a lineup classified as elite will indeed have a positive PMM. We train and test the model on individual player and lineup data from the 2017-18 season and use the model to predict the performance of lineups drawn from all 30 NBA teams' 2018-19 regular season rosters. Although the ANC is conservative and misses some high-performing lineups, it achieves high precision and recommends positionally balanced lineups.

翻译：NBA球队经理和老板们致力于引进高水平球员，而关键决策因素在于新球员与队友的协同表现。本研究旨在识别精英五人大名单——即每分钟净胜分（PMM）为正值的阵容。通过个体球员顺序统计量，我们的模型可识别从未同场竞技的五名球员构成的精英阵容，从而为球员交易决策、薪资谈判及实时战术调整提供依据。我们整合七种分类工具构建一致性分类器（全有或全无分类器，简称ANC），仅当所有七种分类器均预测某阵容为精英时，该阵容才被标记为精英。由此实现高阳性预测值（即精确率），即被归类为精英的阵容实际具有正PMM的可能性。基于2017-18赛季个体球员及阵容数据进行模型训练与测试，并应用于预测2018-19赛季常规赛全部30支NBA球队阵容表现。尽管ANC较为保守且会遗漏部分高效阵容，但其精确率较高，且推荐的阵容在位置分布上更具平衡性。