NBA球队会避免在自己的赛区进行交易吗?

IF 1.4 Q2 SOCIAL SCIENCES, INTERDISCIPLINARY

Network Science Pub Date : 2023-09-18 DOI:10.1017/nws.2023.18

jimi adams, Michał Bojanowski

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引用次数: 0

摘要

在美国职业体育运动中，自己部门内的交易通常被认为是不利的。我们问这种做法有多普遍。为了研究这个问题，我们构建了一个带有日期戳的网络，其中包含了1976年6月至2019年5月期间nba的所有交易。然后，我们使用特定季节加权指数随机图模型来估计团队避免内部交易伙伴的可能性，以及该模式在整个观察期间的一致性。除了实证问题外，这一分析还证明了为统计比较构建适当基线的必要性和难度。我们发现对这种普遍看法的支持有限，甚至没有。

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Do NBA teams avoid trading within their own division?

Abstract Within US professional sports, trades within one’s own division are often perceived to be disadvantageous. We ask how common this practice is. To examine this question, we construct a date-stamped network of all trades in the National Basketball Association between June 1976 and May 2019. We then use season-specific weighted exponential random graph models to estimate the likelihood of teams avoiding within-division trade partners, and how consistent that pattern is across the observed period. In addition to the empirical question, this analysis serves to demonstrate the necessity and difficulty of constructing the proper baseline for statistical comparison. We find limited-to-no support for the popular perception.

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来源期刊

Network Science SOCIAL SCIENCES, INTERDISCIPLINARY-

CiteScore

3.50

自引率

5.90%

发文量

期刊介绍： Network Science is an important journal for an important discipline - one using the network paradigm, focusing on actors and relational linkages, to inform research, methodology, and applications from many fields across the natural, social, engineering and informational sciences. Given growing understanding of the interconnectedness and globalization of the world, network methods are an increasingly recognized way to research aspects of modern society along with the individuals, organizations, and other actors within it. The discipline is ready for a comprehensive journal, open to papers from all relevant areas. Network Science is a defining work, shaping this discipline. The journal welcomes contributions from researchers in all areas working on network theory, methods, and data.