Network Competition and Team Chemistry in the NBA

Research output: Contribution to journalArticle

Abstract

Abstract–We consider a heterogeneous social interaction model where agents interact with peers within their own network but also interact with agents across other (non-peer) networks. To address potential endogeneity in the networks, we assume that each network has a central planner who makes strategic network decisions based on observable and unobservable characteristics of the peers in her charge. The model forms a simultaneous equation system that can be estimated by quasi-maximum likelihood. We apply a restricted version of our model to data on National Basketball Association games, where agents are players, networks are individual teams organized by coaches, and competition is head-to-head. That is, at any time a player only interacts with two networks: their team and the opposing team. We find significant positive within-team peer-effects and both negative and positive opposing-team competitor-effects in NBA games. The former are interpretable as “team chemistries” which enhance the individual performances of players on the same team. The latter are interpretable as “team rivalries,” which can either enhance or diminish the individual performance of opposing players.

Original languageEnglish (US)
Pages (from-to)1-15
Number of pages15
JournalJournal of Business and Economic Statistics
DOIs
StatePublished - 2020

Keywords

  • Endogeneity
  • Machine learning
  • Peer effects
  • Selectivity
  • Spatial autoregression

ASJC Scopus subject areas

  • Statistics and Probability
  • Social Sciences (miscellaneous)
  • Economics and Econometrics
  • Statistics, Probability and Uncertainty

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