Skip to main content
Log in

Pressure and the ability to randomize decision-making: The case of the pickoff play in Major League Baseball

  • Original Paper
  • Published:
Atlantic Economic Journal Aims and scope Submit manuscript

Abstract

In mixed strategy games, the ability to randomize decisions is a critical strategic necessity, yet studies show that such rational behavior is sometimes elusive. This paper examines mixed strategy play in a natural setting, by looking at a pitcher’s decision to throw the ball to home plate or to throw it to first base in a pickoff play. In the absence of significant pressure, we find that pitchers can effectively randomize their sequence of choices to remain unpredictable, as mixed strategy Nash equilibriums require. However, in the face of pressure, some pitchers are less able to randomize their choices. Our paper is the first empirical study in the English language literature to find that decision makers are unable to randomize their strategic decisions when they face an increased cognitive load due to pressure.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Notes

  1. See Oskarsson et al. (2009) for a review of the literature that documents how people have a hard time producing or recognizing random sequences.

  2. This study only examines the situation in which a runner is on first base and no other base.

  3. Thanks to an anonymous reviewer for pointing this out.

  4. The dataset was also limited to games played in American League parks only. The National League teams bunt quite a bit more than they do in the American League, which can confound the data. A bunt is another option to move a runner to second base besides stealing the base. It is probably easier to bunt on a left-handed pitcher since a batter is most likely going to bunt down the first base line. The left-handed pitcher will have his back to first base when he finishes his throw. Therefore it is more difficult for him to field the ball. This gives batters an incentive to bunt more against left-handed pitchers compared to their incentive to bunt against right-handed pitchers. Excluding National League parks eliminates this situation to some extent.

  5. For an easy to read discussion of dynamic probit models see Miranda (2007).

  6. One-tailed tests were used to evaluate the significance of the lagged dependent variable since the literature has found that strategic decisions which are not random have a negative serial correlation pattern. Using the existing literature to inform our approach, the null hypothesis for the estimated coefficients is specified to be greater than or equal to zero, leaving the alternative hypothesis (the sign of the coefficient that we expect) to be negative. See chapter 5 of Studenmund (2011) for a discussion of one-tailed versus two-tailed tests.

  7. It is an interesting question for further research whether the mixed results, concerning whether athletes in different professional sports randomize their decisions, would provide more of a consensus result if the level of pressure were added to the empirical models.

References

  • Allred, S., Duffy, S., & Smith, J. (2016). Cognitive load and strategic sophistication. Journal of Economic Behavior and Organization, 125(May), 162–178.

    Article  Google Scholar 

  • Baddeley, A. (1976). The psychology of memory (pp. 162–187). New York: Basic Books.

    Google Scholar 

  • Baumeister, R., & Showers, C. (1986). A review of paradoxical performance effects: Chocking under pressure in sports and mental tests. European Journal of Social Psychology, 16(October / December), 361–383.

    Article  Google Scholar 

  • Cappelletti, D., Guth, W., & Ploner, M. (2011). Being of two minds: Ultimatum offers under cognitive constraints. Journal of Economic Psychology, 32(6), 940–950.

    Article  Google Scholar 

  • Carpenter, J., Graham, M., & Wolf, J. (2013). Cognitive ability and strategic sophistication. Games and Economic Behavior, 80(1), 115–130.

    Article  Google Scholar 

  • Chiappori, P., Levitt, S., & Groseclose, T. (2002). Testing mixed strategy equilibria when players are heterogeneous: The case of penalty kicks in soccer. American Economic Review, 92(4), 1138–1151.

    Article  Google Scholar 

  • Depken, C., Sonora, R., & Wilson, D. (2012). Performance under pressure: Preliminary evidence from the National Hockey League. International Journal of Sport Finance, 7, 213–231.

    Google Scholar 

  • Downey, J., & McGarrity, J. (2015). Pickoff throws, stolen bases, and southpaws: A comparative static analysis of a mixed strategy game. Atlantic Economic Journal, 43(3), 319–335.

    Article  Google Scholar 

  • Duffy, S., & Smith, J. (2014). Cognitive load in the multi-player prisoner’s dilemma game: Are there brains in games? Journal of Behavioral and Experimental Economics, 51(August), 47–56.

    Article  Google Scholar 

  • Emara, N., Owens, D., Smith, J., & Wilmer, L. (2017). Serial correlation in National Football League Play Calling and its effect on outcomes. Journal of Behavioral and Experimental Economics, 69, 125–132.

    Article  Google Scholar 

  • Eysenck, M., Derakshan, N., Santos, R., & Calvo, M. (2007). Anxiety and cognitive performance: Attentional control theory. Emotion, 7(2), 336–353.

    Article  Google Scholar 

  • Heckman, J. (1981). The incidental parameters problem and the problem of initial conditions in estimating a discrete time-discrete data stochastic process. In C. F. Manski & D. L. McFadden (Eds.), Structural analysis of discrete data and econometric applications (pp. 114–178). Cambridge: MIT Press.

    Google Scholar 

  • Hsu, S., Huang, C., & Tang, C. (2007). Minimax Play at Wimbledon: Comment. The American Economic Review, 97(1), 517–523.

    Article  Google Scholar 

  • Kocher, M., & Sutter, M. (2006). Time is money-time pressure, incentives, and the quality of decision-making. Journal of Economic Behavior and Organization, 61(3), 375–393.

    Article  Google Scholar 

  • Kovash, K., & Levitt, S. (2009). Professionals Do Not Play Minimax: Evidence from Major League Baseball and the National Football League. NBER Working paper series, September, https://www.nber.org/papers/w15347.pdf. Last date Accessed: January 15, 2018.

  • La Russa, T. (2012). One Last Strike (pp. 325–341). New York: HarperCollins.

    Google Scholar 

  • Leder, J., Alexander, J., & Mojzisch, A. (2013). Stress and strategic decision-making in a beauty contest game. Psychoneuroendocrinology, 38(9), 1503–1511.

    Article  Google Scholar 

  • Levitt, S., & List, S. (2008). Homo economicus Evolves. Science, 319(5865), 909–910.

    Article  Google Scholar 

  • Masicampo, E., & Baumeister, R. (2008). Toward a physiology of duel-process reasoning and judgement. Psychological Science, 19(3), 255–260.

    Article  Google Scholar 

  • McGarrity, J., & Linnen, B. (2010). Pass or run: An empirical test of the matching pennies game using data from the National Football League. Southern Economic Journal, 76(3), 791–810.

    Article  Google Scholar 

  • Miranda, A. (2007). Dynamic probit models for panel data: A comparison of three methods of estimation. 2007 UK Stata users group meeting. https://www.stata.com/meeting/13uk/miranda_Dprob_pe.pdf. Last date Accessed: Febrary 9, 2018.

  • Oskarsson, A., Van Boven, T., & McClelland, G. (2009). What’s next? Judging sequences of binary events. Psychology Bulletin, 135(2), 262–285.

    Article  Google Scholar 

  • Palacios-Huerta, I. (2003). Professionals play minimax. Review of Economic Studies, 70, 395–415.

    Article  Google Scholar 

  • Passerman, M. (2010). Gender differences in performance in competitive environments? Evidence from professional tennis players. NBER Working paper, https://conference.nber.org/conferences/2007/si2007/LS/paserman.pdf. Last date Accessed: Febrary 9, 2018.

  • Porcelli, A., & Delgado, M. (2009). Acute stress modulates risk taking in financial decision-making. Psychological Science, 20(3), 278–283.

    Article  Google Scholar 

  • Schoofs, D., & Wolf, O. (2009). Cold pressor stress impairs performance on working memory tasks requiring executive functions in health young men. Behavioral Neuroscience, 123(5), 1066–1075.

    Article  Google Scholar 

  • Smith, D.. (2018). Game Logs. Retrosheet.org. (https://www.retrosheet.org/). Last date Accessed: August 20, 2019.

  • Studenmund, A. (2011). Using econometrics. Boston: Addison-Wesley.

    Google Scholar 

  • Sutter, M., Kocher, M., & Staub, S. (2003). Bargaining under time pressure in an experimental game. Economic Letters, 81(3), 341–347.

    Article  Google Scholar 

  • Tversky, A., & Kahneman, D. (1974). Judgement under uncertainty: Heuristics and biases. Science, 185(4157), 1124–1131.

    Article  Google Scholar 

  • Walker, M., & Wooders, J. (2001). Minimax play at Wimbledon. American Economic Review, 91(5), 1521–1538.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Joseph McGarrity.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

ESM 1

(DOCX 13 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Downey, J., McGarrity, J. Pressure and the ability to randomize decision-making: The case of the pickoff play in Major League Baseball. Atl Econ J 47, 261–274 (2019). https://doi.org/10.1007/s11293-019-09631-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11293-019-09631-8

Keywords

JEL

Navigation