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Informed voters and electoral outcomes: a natural experiment stemming from a fundamental information-technological shift

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Abstract

Do informed electorates choose better candidates? While that question is straightforward, its answer often is elusive. Typically, candidate-quality information is neither salient nor subject to exogenous change. We identify a natural experiment within a non-political election setting that is transparent and features exogenous change in the candidate-quality information frontier. The setting is Major League Baseball’s (MLB) annual selection of two most valuable players, a challenging environment with an innately heterogeneous candidate set, and the exogenous change is the development of the pathbreaking, comprehensive player-value measure Wins Above Replacement (WAR) in 2004 and its subsequent calculation for all retrospective MLB player-seasons. WAR's development and rapid popularization informed voting from 2004 onward. Retrospective calculation allows us to draw back the curtain and evaluate how pre-2004 voters behaved with respect to revealed candidate quality. From negative binomial, fixed-effect regression models, we find robust evidence of significant, substantial, pivotal behavioral change on the part of voters since 2004.

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Code is unpublished.

Notes

  1. Some teams adopt MVP awards as an incentive mechanism. Some players are offered large contractual bonuses for winning being selected as an MVP. For example, star MLB player Mike Trout earns an additional $500,000 by his team, the Los Angeles Angels, in the event of winning an MVP award.

  2. We thank an anonymous editor for pointing this out.

  3. The formula for position players is WAR = (Batting Runs + Base Running Runs + Fielding Runs + Defensive Runs Saved + Position Adjustment + Replacement Runs)/(Average Runs Needed to Obtain an Additional Win).

  4. The formula for pitchers is WAR = [(RA9avg—RA9)*(IP/9) + Rlr]/(Rpw) where RA9avg is the number of runs an average pitcher is expected to give up in 9 innings, RA9 is the average number of runs given up per nine innings, IP is the number of innings pitched, Rlr is replacement level runs, and Rpw is the average number of runs needed to obtain an additional win.

  5. A teammate effect is, however, possible. For instance, if a good player has an even better player hitting behind him in the batting order, it is plausible the first batter will receive better pitches to hit and would therefore produce more runs and wins (which is not captured by WAR).

  6. Young (1995) discusses Borda voting and its implications for the likelihood of identifying the “correct” (Condorcet) top candidate should one exist.

  7. In our dataset, the NL and AL were imbalanced in terms of numbers of teams from 1998 (when the Milwaukee Brewers moved to the NL) through 2012 (after which the Houston Astros moved to the AL). That imbalance affected the number of ballots cast for each league during those years. We account for it empirically by re-scaling ballots to a maximum of 420 possible points for each player during those seasons.

  8. Many of the counterparts are earlier versions of the same person.

  9. More generally, sport often is used to measure the empirical incidence of aggregation paradoxes. For a recent contribution along those lines, see, e.g., Boudreau et al. (2018).

  10. Negative binomial models are chosen over Poisson models owing to strong evidence of overdispersion in vote-count data.

  11. All WAR measures use the same inputs but slightly different specifications.

  12. Specifically, the measure was designed for description rather than for forecasting. Baseball Reference Founder and CEO Sean Forman confirmed that point in a conversation with two of the present authors. Of course, retrospective analysis of the recent season is an input into MVP decision-making.

  13. We do not control for game characteristics because it is unlikely that they will affect an individual’s WAR score and MVP votes. The reason is that because if a player is a season’s WAR leader, he always has participated in a large number of games (n > 100). Over the course of a season, we therefore expect game characteristics to revert to the mean against an assortment of different opponents.

  14. We thank an anonymous reviewer for providing those thoughtful comments.

  15. Drazen and Eslava 2010 study voting scenarios based on a basket of issues.

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Acknowledgments

We would like to thank William Shughart (and other members of the 2019 Public Choice Society Meetings), Marek Kaminski, and anonymous referees for valuable feedback and suggestions. For contributions during the early manuscript phase, we would also like to thank directors and judges of the Carnegie Mellon Sport Analytics Research Competition, as well as Brian Taylor, Shana Gadarian, Simon Weschle (and other faculty members and graduate students at the SU Maxwell School Department of Political Science), Justin Ross, B. Andrew Chupp, and Seth Freedman (and other faculty members and graduate students at the IU School of Public and Environmental Affairs). We would also like to thank Lara J. Potter for her encouragement.

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This research was not funded.

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Authors and Affiliations

  1. Syracuse University, 301 MacNaughton Hall, #316, Syracuse, NY, 13244, USA

    Shane Sanders

  2. University of North Georgia, 105 Barnes Hall, Dahlonega, GA, 30597, USA

    Joel Potter

  3. Syracuse University, 300 MacNaughton Hall, #320, Syracuse, NY, 13244, USA

    Justin Ehrlich

  4. Pittsburgh Pirates, Pittsburgh, PA, USA

    Justin Perline

  5. Florida Atlantic University, 777 Glades Road, Kaye Hall 145, Boca Raton, FL, 33431, USA

    Christopher Boudreaux

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  1. Shane Sanders
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  2. Joel Potter
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  3. Justin Ehrlich
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  4. Justin Perline
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Correspondence to Shane Sanders.

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Sanders, S., Potter, J., Ehrlich, J. et al. Informed voters and electoral outcomes: a natural experiment stemming from a fundamental information-technological shift. Public Choice 189, 257–277 (2021). https://doi.org/10.1007/s11127-021-00884-z

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