In basketball, measures of individual player performance provide critical guidance for a broad spectrum of decisions related to training and game strategy. However, most studies on this topic focus on performance level measurement, neglecting other important factors, such as performance variability. Here we model shooting performance variability by using Markov switching models, assuming the existence of two alternating performance regimes related to the positive or negative synergies that specific combinations of players may create on the court. The main goal of this analysis is to investigate the relationships between each player's performance variability and team line‐up composition by assuming shot‐varying transition probabilities between regimes. Relationships between pairs of players are then visualized in a network graph, highlighting positive and negative interactions between teammates. On the basis of these interactions, we build a score for the line‐ups, which we show correlates with the line‐up's shooting performance. This confirms that interactions between teammates detected by the Markov switching model directly affect team performance, which is information that would be enormously useful to coaches when deciding which players should play together.

中文翻译：

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篮球投篮表现变异性和队友互动的马尔可夫切换模型

在篮球比赛中，个人球员表现的衡量标准为与训练和比赛策略相关的广泛决策提供了关键指导。但是，有关此主题的大多数研究都集中在性能水平测量上，而忽略了其他重要因素，例如性能可变性。在这里，我们使用马尔科夫切换模型来模拟射击表现的可变性，假设存在两种交替的表现方式，这些表现方式涉及特定的球员组合可能在球场上产生的正面或负面协同效应。该分析的主要目标是通过假设不同政权之间的投篮转变概率来研究每个球员的表现变异性与团队阵容组成之间的关系。然后，在网络图中将玩家对之间的关​​系可视化，强调队友之间的正面和负面互动。在这些互动的基础上，我们为阵容建立了得分，并显示出与阵容的射门表现相关。这证实了由马尔可夫转换模型检测到的队友之间的互动会直接影响球队的表现，这一信息对于教练确定哪些球员应该一起比赛时非常有用。