nep-spo New Economics Papers
on Sports and Economics
Issue of 2022‒07‒25
two papers chosen by
Humberto Barreto
DePauw University

  1. Nothing Propinks Like Propinquity: Using Machine Learning to Estimate the Effects of Spatial Proximity in the Major League Baseball Draft By Majid Ahmadi; Nathan Durst; Jeff Lachman; John List; Mason List; Noah List; Atom Vayalinkal
  2. Skewness preferences: Evidence from online poker By Dertwinkel-Kalt, Markus; Kasinger, Johannes; Schneider, Dmitrij

  1. By: Majid Ahmadi; Nathan Durst; Jeff Lachman; John List; Mason List; Noah List; Atom Vayalinkal
    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
    URL: http://d.repec.org/n?u=RePEc:feb:artefa:00758&r=
  2. By: Dertwinkel-Kalt, Markus; Kasinger, Johannes; Schneider, Dmitrij
    Abstract: We investigate what statistical properties drive risk-taking in a large set of observational panel data on online poker games (n=4,450,585). Each observation refers to a choice between a safe "insurance" option and a binary lottery of winning or losing the game. Our setting offers a real-world choice situation with substantial incentives where probability distributions are simple, transparent, and known to the individuals. We find that individuals reveal a strong and robust preference for skewness. The effect of skewness is most pronounced among experienced and losing players but remains highly significant for winning players, in contrast to the variance effect.
    Keywords: Online Poker,Risk Attitudes,Risk Preferences,Choice under Risk
    JEL: D01 D81 G40
    Date: 2022
    URL: http://d.repec.org/n?u=RePEc:zbw:safewp:351&r=

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