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An extended regularized adjusted plus-minus analysis for lineup management in basketball using play-by-play data
IMA Journal of Management Mathematics ( IF 1.9 ) Pub Date : 2020-10-15 , DOI: 10.1093/imaman/dpaa022
Luca Grassetti 1 , Ruggero Bellio 1 , Luca Di Gaspero 2 , Giovanni Fonseca 1 , Paolo Vidoni 1
Affiliation  

In this work we analyse basketball play-by-play data in order to evaluate the efficiency of different five-man lineups employed by teams. Starting from the adjusted plus-minus framework, we present a model-based strategy for the analysis of the result of partial match outcomes, extending the current literature in two main directions. The first extension replaces the classical response variable (scored points) with a comprehensive score that combines a set of box score statistics. This allows various aspects of the game to be separated. The second extension focuses on entire lineups rather than individual players, using a suitable extended model specification. The model fitting procedure is Bayesian and provides the necessary regularization. An advantage of this approach is the use of posterior distributions to rank players and lineups, providing an effective tool for team managers. For the empirical analysis, we use the 2018/2019 regular season of the Turkish Airlines Euroleague Championship, with play-by-play and box scores for 240 matches, which are made available by the league website. The results of the model fitting can be used for several investigations as, for instance, the comparative analysis of the effects of single players and the estimation of total and synergic effects of lineups monitoring. Moreover, the behaviour of players and lineups during the season, updating the estimation results after each gameday, can represent a rather useful tool.

中文翻译:

使用逐场比赛数据进行篮球阵容管理的扩展正则化调整正负分析

在这项工作中,我们分析了篮球比赛数据,以评估球队采用的不同五人阵容的效率。从调整后的加减框架开始,我们提出了一种基于模型的策略来分析部分匹配结果的结果,将当前文献扩展到两个主要方向。第一个扩展将经典响应变量(得分点)替换为综合得分,该得分结合了一组框得分统计数据。这允许分离游戏的各个方面。第二个扩展侧重于整个阵容而不是单个球员,使用合适的扩展模型规范。模型拟合程序是贝叶斯的,并提供必要的正则化。这种方法的一个优点是使用后验分布对球员和阵容进行排名,为团队管理者提供有效的工具。对于实证分析,我们使用了 2018/2019 年土耳其航空欧洲联赛冠军赛的常规赛,包括联赛网站提供的 240 场比赛的逐场比赛和比分。模型拟合的结果可用于多项调查,例如,单人效应的比较分析以及阵容监控的总体和协同效应的估计。此外,赛季期间球员和阵容的行为,在每个比赛日之后更新估计结果,可以代表一个相当有用的工具。由联盟网站提供。模型拟合的结果可用于多项调查,例如,单人效应的比较分析以及阵容监控的总体和协同效应的估计。此外,赛季期间球员和阵容的行为,在每个比赛日之后更新估计结果,可以代表一个相当有用的工具。由联盟网站提供。模型拟合的结果可用于多项调查,例如,单人效应的比较分析以及阵容监控的总体和协同效应的估计。此外,赛季期间球员和阵容的行为,在每个比赛日之后更新估计结果,可以代表一个相当有用的工具。
更新日期:2020-10-15
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