Nothing Propinks Like Propinquity: Using Machine Learning to Estimate the Effects of Spatial Proximity in the Major League Baseball Draft

Majid Ahmadi, Nathan Durst, Jeff Lachman, Mason List, Noah List, John List and Atom Vayalinkal ()

Artefactual Field Experiments from The Field Experiments Website

Abstract: Recent models and empirical work on network formation emphasize the importance of propinquity in producing strong interpersonal connections. Yet, one might wonder how deep such insights run, as thus far empirical results rely on survey and lab-based evidence. In this study, we examine propinquity in a high-stakes setting of talent allocation: the Major League Baseball (MLB) Draft. We examine draft picks from 2000-2019 across every MLB club of the nearly 30,000 players drafted (from a player pool of more than a million potential draftees). Our findings can be summarized in three parts. First, propinquity is alive and well in our setting, and spans even the latter years of our sample, when higher-level statistical exercises have become the norm rather than the exception. Second, the measured effect size is important, as MLB clubs pay a real cost in terms of inferior talent acquired due to propinquity bias: for example, their draft picks appear in 25 fewer games relative to teams that do not exhibit propinquity bias. Finally, the effect is found to be the most pronounced in later rounds of the draft (after round 15), where the Scouting Director has the greatest latitude.

Date: 2022
New Economics Papers: this item is included in nep-big, nep-cmp and nep-spo
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Working Paper: Nothing Propinks Like Propinquity: Using Machine Learning to Estimate the Effects of Spatial Proximity in the Major League Baseball Draft (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:feb:artefa:00758

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