IDEAS home Printed from https://ideas.repec.org/a/sae/jospec/v17y2016i7p661-671.html
   My bibliography  Save this article

Predicting the Winner of Tied National Football League Games

Author

Listed:
  • Jared Quenzel
  • Paul Shea

Abstract

We construct a data set of all 429 tied at the half regular season National Football League (NFL) games between 1994 and 2012. We then examine whether or not the path taken to reach the tie (e.g., rushing yards, turnovers, etc.) has any ability to predict the eventual winner. Our main finding is that only the point spread is significantly predictive, although there is weak evidence to suggest that allowing more sacks reduces the chances of winning. Surprisingly, we find that the team receiving the first possession of the second half does not enjoy a statistically significant advantage. Teams should thus simply try to maximize their first half lead without expecting that first half strategies such as “establishing the run†will pay dividends in the second half.

Suggested Citation

  • Jared Quenzel & Paul Shea, 2016. "Predicting the Winner of Tied National Football League Games," Journal of Sports Economics, , vol. 17(7), pages 661-671, October.
    • Handle: RePEc:sae:jospec:v:17:y:2016:i:7:p:661-671
    DOI: 10.1177/1527002514539688
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/1527002514539688
    Download Restriction: no
    File URL: https://libkey.io/10.1177/1527002514539688?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Jeremy Sandford & Paul Shea, 2013. "Optimal Setting of Point Spreads," Economica, London School of Economics and Political Science, vol. 80(317), pages 149-170, January.
    2. Alamar Benjamin C, 2010. "Measuring Risk in NFL Playcalling," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 6(2), pages 1-9, April.
    3. Alamar Benjamin C, 2006. "The Passing Premium Puzzle," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 2(4), pages 1-10, October.
    4. 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.
    5. Rockerbie Duane W., 2008. "The Passing Premium Puzzle Revisited," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 4(2), pages 1-13, April.
    6. 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.
    7. Steven D. Levitt, 2004. "Why are gambling markets organised so differently from financial markets?," Economic Journal, Royal Economic Society, vol. 114(495), pages 223-246, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Justin Cox & Adam L. Schwartz & Bonnie F. Van Ness & Robert A. Van Ness, 2021. "The Predictive Power of College Football Spreads: Regular Season Versus Bowl Games," Journal of Sports Economics, , vol. 22(3), pages 251-273, April.
    2. Yazan F. Roumani, 2023. "Sports analytics in the NFL: classifying the winner of the superbowl," Annals of Operations Research, Springer, vol. 325(1), pages 715-730, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Urschel John D & Zhuang Jun, 2011. "Are NFL Coaches Risk and Loss Averse? Evidence from Their Use of Kickoff Strategies," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 7(3), pages 1-17, July.
    3. McGough Erin & Clemons Curtis & Ferrara Michael & Norfolk Timothy & Young Gerald W, 2010. "A Game-Theoretic Approach to Personnel Decisions in American Football," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 6(4), pages 1-15, October.
    4. Emara, Noha & Owens, David & Smith, John & Wilmer, Lisa, 2014. "Minimax on the gridiron: Serial correlation and its effects on outcomes in the National Football League," MPRA Paper 58907, University Library of Munich, Germany.
    5. Yurko Ronald & Ventura Samuel & Horowitz Maksim, 2019. "nflWAR: a reproducible method for offensive player evaluation in football," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 15(3), pages 163-183, September.
    6. Arne Feddersen & Brad R. Humphreys & Brian P. Soebbing, 2018. "Sentiment Bias in National Basketball Association Betting," Journal of Sports Economics, , vol. 19(4), pages 455-472, May.
    7. Paserman, M. Daniele, 2023. "Gender Differences in Performance in Competitive Environments? Evidence from Professional Tennis Players," Journal of Economic Behavior & Organization, Elsevier, vol. 212(C), pages 590-609.
    8. Alamar Benjamin C, 2010. "Measuring Risk in NFL Playcalling," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 6(2), pages 1-9, April.
    9. Snyder Kevin & Lopez Michael, 2015. "Consistency, accuracy, and fairness: a study of discretionary penalties in the NFL," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 11(4), pages 219-230, December.
    10. Michael William Gmeiner, 2019. "History-Dependent Mixed Strategies: Evidence From Major League Baseball," Journal of Sports Economics, , vol. 20(3), pages 371-398, April.
    11. Lefgren, Lars J. & Platt, Brennan & Price, Joseph & Higbee, Samuel, 2019. "Outcome based accountability: Theory and evidence," Journal of Economic Behavior & Organization, Elsevier, vol. 160(C), pages 121-137.
    12. Klaassen, Franc J.G.M. & Magnus, Jan R., 2009. "The efficiency of top agents: An analysis through service strategy in tennis," Journal of Econometrics, Elsevier, vol. 148(1), pages 72-85, January.
    13. Heiny Erik L & Blevins David, 2011. "Predicting the Atlanta Falcons Play-Calling Using Discriminant Analysis," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 7(3), pages 1-14, July.
    14. Benjamin Williams & Will Palmquist & Ryan Elmore, 2023. "Simulation-based decision making in the NFL using NFLSimulatoR," Annals of Operations Research, Springer, vol. 325(1), pages 731-742, June.
    15. Lopez Michael J., 2020. "Bigger data, better questions, and a return to fourth down behavior: an introduction to a special issue on tracking datain the National football League," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 16(2), pages 73-79, June.
    16. Andrew Weinbach & Rodney J. Paul, 2009. "National television coverage and the behavioural bias of bettors: the American college football totals market," International Gambling Studies, Taylor & Francis Journals, vol. 9(1), pages 55-66, April.
    17. repec:hum:wpaper:sfb649dp2008-032 is not listed on IDEAS
    18. Barge-Gil, Andrés & García-Hiernaux, Alfredo, 2019. "Staking plans in sports betting under unknown true probabilities of the event," MPRA Paper 92196, University Library of Munich, Germany.
    19. Egon Franck & Erwin Verbeek & Stephan Nüesch, 2011. "Sentimental Preferences and the Organizational Regime of Betting Markets," Southern Economic Journal, John Wiley & Sons, vol. 78(2), pages 502-518, October.
    20. Jeremy Sandford & Paul Shea, 2013. "Optimal Setting of Point Spreads," Economica, London School of Economics and Political Science, vol. 80(317), pages 149-170, January.
    21. Fort, Rodney & Maxcy, Joel & Diehl, Mark, 2016. "Uncertainty by regulation: Rottenberg׳s invariance principle," Research in Economics, Elsevier, vol. 70(3), pages 454-467.

    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:sae:jospec:v:17:y:2016:i:7:p:661-671. See general information about how to correct material in RePEc.

    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 bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.