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Network Competition and Team Chemistry in the NBA

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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.

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  • William C. Horrace & Hyunseok Jung & Shane Sanders, 2020. "Network Competition and Team Chemistry in the NBA," Center for Policy Research Working Papers 226, Center for Policy Research, Maxwell School, Syracuse University.
  • Handle: RePEc:max:cprwps:226
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    Cited by:

    1. William C. Horrace & Hyunseok Jung & Jonathan L. Pressler & Amy Ellen Schwartz, 2021. "What Makes a Classmate a Peer? Examining Which Peers Matter in NYC Elementary Schools," Center for Policy Research Working Papers 241, Center for Policy Research, Maxwell School, Syracuse University.

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    More about this item

    Keywords

    Spatial Analysis; Peer Effects; Endogeneity; Machine Learning;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity

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