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

32 Pages Posted: 5 Jan 2023

See all articles by Majid Ahmadi

Majid Ahmadi

University of Chicago

Nathan Durst

Chicago White Sox; National Cross-Checker

Jeff Lachman

Chicago White Sox; National Cross-Checker

John A. List

University of Chicago - Department of Economics

Mason List

University of Chicago

Noah List

Harvard University

Atom Vayalinkal

University of Toronto

Multiple version iconThere are 2 versions of this paper

Date Written: December 26, 2022

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 from 2000-2019 (30,000 players were drafted from a player pool of more than a million potential draftees). Our findings can be summarized in four 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 consequential, as MLB clubs pay a significant opportunity cost in terms of inferior talent acquired due to propinquity bias: for example, their draft picks are 38% less likely to ever play a MLB game relative to players drafted without propinquity bias. Third, those players who benefit from propinquity bias fare better both in terms of the timing of their draft picks and their initial financial contract, conditional on draft order. Finally, the effect is found to be the most pronounced in later rounds of the draft, where the Scouting Director has the greatest latitude.

JEL Classification: C93,D4,J30,J7

Suggested Citation

Ahmadi, Majid and Durst, Nathan and Lachman, Jeff and List, John A. and List, Mason and List, Noah and Vayalinkal, Atom, Nothing Propinks Like Propinquity: Using Machine Learning to Estimate the Effects of Spatial Proximity in the Major League Baseball Draft (December 26, 2022). University of Chicago, Becker Friedman Institute for Economics Working Paper No. 2022-169, Available at SSRN: https://ssrn.com/abstract=4318145 or http://dx.doi.org/10.2139/ssrn.4318145

Majid Ahmadi

University of Chicago ( email )

1101 East 58th Street
Chicago, IL 60637
United States

Nathan Durst

Chicago White Sox ( email )

National Cross-Checker ( email )

Jeff Lachman

Chicago White Sox ( email )

National Cross-Checker ( email )

John A. List (Contact Author)

University of Chicago - Department of Economics ( email )

1126 East 59th Street
Chicago, IL 60637
United States

Mason List

University of Chicago ( email )

1101 East 58th Street
Chicago, IL 60637
United States

Noah List

Harvard University ( email )

1875 Cambridge Street
Cambridge, MA 02138
United States

Atom Vayalinkal

University of Toronto ( email )

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