IDEAS home Printed from https://ideas.repec.org/a/eee/soceco/v69y2017icp125-132.html
   My bibliography  Save this article

Serial correlation in National Football League play calling and its effects on outcomes

Author

Listed:
  • Emara, Noha
  • Owens, David
  • Smith, John
  • Wilmer, Lisa

Abstract

We investigate the strategic behavior of highly informed agents playing zero-sum games under highly incentivized conditions. We examine data from 3455 National Football League (NFL) games from the 2000 season through the 2012 season, and categorize each play as “rush” or a “pass.” We find that the pass-rush mix exhibits negative serial correlation: play types alternate more frequently than an independent stochastic process. This is a seemingly exploitable strategy, and we find that this serial correlation, according to two distinct measures, negatively affects play efficacy. Our analysis suggests that teams could profit from more clustered play selections, which switch play type less frequently. Our results are consistent with teams excessively switching play types in an effort to be perceived as unpredictable.

Suggested Citation

  • Emara, Noha & Owens, David & Smith, John & Wilmer, Lisa, 2017. "Serial correlation in National Football League play calling and its effects on outcomes," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 69(C), pages 125-132.
  • Handle: RePEc:eee:soceco:v:69:y:2017:i:c:p:125-132
    DOI: 10.1016/j.socec.2017.01.007
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S2214804317300071
    Download Restriction: Full text for ScienceDirect subscribers only

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Robert W. Rosenthal & Jason Shachat & Mark Walker, 2003. "Hide and seek in Arizona," International Journal of Game Theory, Springer;Game Theory Society, vol. 32(2), pages 273-293, December.
    2. Geng, Sen & Peng, Yujia & Shachat, Jason & Zhong, Huizhen, 2015. "Adolescents, cognitive ability, and minimax play," Economics Letters, Elsevier, vol. 128(C), pages 54-58.
    3. Walker, Mark & Wooders, John & Amir, Rabah, 2011. "Equilibrium play in matches: Binary Markov games," Games and Economic Behavior, Elsevier, vol. 71(2), pages 487-502, March.
    4. Konstantinos Drakos & Panagiotis Th. Konstantinou, 2013. "Investment decisions in manufacturing: assessing the effects of real oil prices and their uncertainty," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(1), pages 151-165, January.
    5. Pierre‐Carl Michaud & Konstantinos Tatsiramos, 2011. "Fertility and female employment dynamics in Europe: the effect of using alternative econometric modeling assumptions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(4), pages 641-668, June.
    6. Okano, Yoshitaka, 2013. "Minimax play by teams," Games and Economic Behavior, Elsevier, vol. 77(1), pages 168-180.
    7. Steven D. Levitt & John A. List & David H. Reiley, 2010. "What Happens in the Field Stays in the Field: Exploring Whether Professionals Play Minimax in Laboratory Experiments," Econometrica, Econometric Society, vol. 78(4), pages 1413-1434, July.
    8. Mookherjee Dilip & Sopher Barry, 1994. "Learning Behavior in an Experimental Matching Pennies Game," Games and Economic Behavior, Elsevier, vol. 7(1), pages 62-91, July.
    9. Van Essen, Matt & Wooders, John, 2015. "Blind stealing: Experience and expertise in a mixed-strategy poker experiment," Games and Economic Behavior, Elsevier, vol. 91(C), pages 186-206.
    10. Jeffrey M. Wooldridge, 2005. "Simple solutions to the initial conditions problem in dynamic, nonlinear panel data models with unobserved heterogeneity," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(1), pages 39-54.
    11. Spiliopoulos, Leonidas, 2012. "Pattern recognition and subjective belief learning in a repeated constant-sum game," Games and Economic Behavior, Elsevier, vol. 75(2), pages 921-935.
    12. Ignacio Palacios-Huerta & Oscar Volij, 2008. "Experientia Docet: Professionals Play Minimax in Laboratory Experiments," Econometrica, Econometric Society, vol. 76(1), pages 71-115, January.
    13. Ochs Jack, 1995. "Games with Unique, Mixed Strategy Equilibria: An Experimental Study," Games and Economic Behavior, Elsevier, vol. 10(1), pages 202-217, July.
    14. Rockerbie Duane W., 2008. "The Passing Premium Puzzle Revisited," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 4(2), pages 1-13, April.
    15. John Wooders, 2010. "Does Experience Teach? Professionals and Minimax Play in the Lab," Econometrica, Econometric Society, vol. 78(3), pages 1143-1154, May.
    16. Kenneth Kovash & Steven D. Levitt, 2009. "Professionals Do Not Play Minimax: Evidence from Major League Baseball and the National Football League," NBER Working Papers 15347, National Bureau of Economic Research, Inc.
    17. Luigi Buzzacchi & Stefano Pedrini, 2014. "Does player specialization predict player actions? Evidence from penalty kicks at FIFA World Cup and UEFA Euro Cup," Applied Economics, Taylor & Francis Journals, vol. 46(10), pages 1067-1080, April.
    18. Mookherjee, Dilip & Sopher, Barry, 1997. "Learning and Decision Costs in Experimental Constant Sum Games," Games and Economic Behavior, Elsevier, vol. 19(1), pages 97-132, April.
    19. Alamar Benjamin C, 2010. "Measuring Risk in NFL Playcalling," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 6(2), pages 1-9, April.
    20. Bar-Eli, Michael & Azar, Ofer H. & Ritov, Ilana & Keidar-Levin, Yael & Schein, Galit, 2007. "Action bias among elite soccer goalkeepers: The case of penalty kicks," Journal of Economic Psychology, Elsevier, vol. 28(5), pages 606-621, October.
    21. Alamar Benjamin C, 2006. "The Passing Premium Puzzle," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 2(4), pages 1-10, October.
    22. Joseph P. McGarrity & Brian Linnen, 2010. "Pass or Run: An Empirical Test of the Matching Pennies Game Using Data from the National Football League," Southern Economic Journal, Southern Economic Association, vol. 76(3), pages 791-810, January.
    23. Jacob Bundrick & Joseph McGarrity, 2014. "Strategic Play in the NFL’s Offensive Play Calling," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 20(3), pages 339-340, August.
    24. David Romer, 2006. "Do Firms Maximize? Evidence from Professional Football," Journal of Political Economy, University of Chicago Press, vol. 114(2), pages 340-365, April.
    25. Brown, James N & Rosenthal, Robert W, 1990. "Testing the Minimax Hypothesis: A Re-examination of O'Neill's Game Experiment," Econometrica, Econometric Society, vol. 58(5), pages 1065-1081, September.
    26. Wiji Arulampalam & Mark B. Stewart, 2009. "Simplified Implementation of the Heckman Estimator of the Dynamic Probit Model and a Comparison with Alternative Estimators," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(5), pages 659-681, October.
    27. Ofer Azar & Michael Bar-Eli, 2011. "Do soccer players play the mixed-strategy Nash equilibrium?," Applied Economics, Taylor & Francis Journals, vol. 43(25), pages 3591-3601.
    28. Rapoport, Amnon & Amaldoss, Wilfred, 2004. "Mixed-strategy play in single-stage first-price all-pay auctions with symmetric players," Journal of Economic Behavior & Organization, Elsevier, vol. 54(4), pages 585-607, August.
    29. Halpern, Joseph Y. & Pass, Rafael, 2015. "Algorithmic rationality: Game theory with costly computation," Journal of Economic Theory, Elsevier, vol. 156(C), pages 246-268.
    30. Jason Shachat & J. Todd Swarthout, 2004. "Do we detect and exploit mixed strategy play by opponents?," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 59(3), pages 359-373, July.
    31. Binmore, Ken & Swierzbinski, Joe & Proulx, Chris, 2001. "Does Minimax Work? An Experimental Study," Economic Journal, Royal Economic Society, vol. 111(473), pages 445-464, July.
    32. Ignacio Palacios-Huerta, 2003. "Professionals Play Minimax," Review of Economic Studies, Oxford University Press, vol. 70(2), pages 395-415.
    33. Mark Walker & John Wooders, 2001. "Minimax Play at Wimbledon," American Economic Review, American Economic Association, vol. 91(5), pages 1521-1538, December.
    34. repec:kap:iaecre:v:20:y:2014:i:3:p:339-340 is not listed on IDEAS
    35. Matthew Rabin, 2002. "Inference by Believers in the Law of Small Numbers," The Quarterly Journal of Economics, Oxford University Press, vol. 117(3), pages 775-816.
    36. repec:feb:artefa:0094 is not listed on IDEAS
    37. Steven Levitt & John List & David Reiley, 2010. "What happens in the field stays in the field: Professionals do not play minimax in laboratory experiments," Artefactual Field Experiments 00080, The Field Experiments Website.
    38. P.-A. Chiappori, 2002. "Testing Mixed-Strategy Equilibria When Players Are Heterogeneous: The Case of Penalty Kicks in Soccer," American Economic Review, American Economic Association, vol. 92(4), pages 1138-1151, September.
    39. Shachat, Jason M., 2002. "Mixed Strategy Play and the Minimax Hypothesis," Journal of Economic Theory, Elsevier, vol. 104(1), pages 189-226, May.
    40. Rapoport, Amnon & Amaldoss, Wilfred, 2000. "Mixed strategies and iterative elimination of strongly dominated strategies: an experimental investigation of states of knowledge," Journal of Economic Behavior & Organization, Elsevier, vol. 42(4), pages 483-521, August.
    41. Rapoport, Amnon & Boebel, Richard B., 1992. "Mixed strategies in strictly competitive games: A further test of the minimax hypothesis," Games and Economic Behavior, Elsevier, vol. 4(2), pages 261-283, April.
    42. Shih-Hsun Hsu & Chen-Ying Huang & Cheng-Tao Tang, 2007. "Minimax Play at Wimbledon: Comment," American Economic Review, American Economic Association, vol. 97(1), pages 517-523, March.
    43. repec:spr:compst:v:59:y:2004:i:3:p:359-373 is not listed on IDEAS
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    Serial correlation; Game theory; Mixed strategies; Matching pennies;

    JEL classification:

    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments
    • D03 - Microeconomics - - General - - - Behavioral Microeconomics: Underlying Principles

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:soceco:v:69:y:2017:i:c:p:125-132. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dana Niculescu). General contact details of provider: http://www.elsevier.com/locate/inca/620175 .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.