This paper analyzes the link between relative market value of representative subsets of athletes in the National Basketball Association (NBA) and individual wages. NBA athletes are categorized with respect to multiple performance characteristics utilizing the k-means algorithm to cluster observations and a group's market value is calculated by averaging real annual salaries. Employing GMM estimation techniques to a dynamic wage equation, we find a statistically significant and positive effect of one-period lagged relative market value of an athlete's representative cluster on individual wages after controlling for past individual performance. This finding is consistent with the theory of prototype heuristic, introduced by Kahneman and Frederick (2002), that NBA teams' judgment about an athlete's future performance is based on a comparison of the player to a prototype group consisting of other but comparable athletes. ud
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机译：本文分析了美国国家篮球协会（NBA）运动员代表子集的相对市场价值与个人工资之间的联系。 NBA运动员使用k均值算法对观察结果的多种表现特征进行了分类，以对观察结果进行聚类，并通过平均实际年薪来计算组的市场价值。通过将GMM估算技术应用于动态工资方程，我们发现在控制了过去的个人表现后，运动员代表集群的一期滞后相对市场价值对个人工资具有统计学意义的积极影响。这一发现与卡纳曼和弗雷德里克（Kahneman and Frederick（2002）提出的原型启发式理论）一致，即NBA球队对运动员未来表现的判断是基于将运动员与其他但可比较的运动员组成的原型组进行比较得出的。 ud
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