IDEAS home Printed from https://ideas.repec.org/a/bpj/jqsprt/v11y2015i1p53-67n1.html
   My bibliography  Save this article

A new approach to bracket prediction in the NCAA Men’s Basketball Tournament based on a dual-proportion likelihood

Author

Listed:
  • Gupta Ajay Andrew

    (Statistics, The Florida State University, 117 N. Woodward Ave. P.O. Box 3064330, Tallahassee, FL 32306, USA)

Abstract

The widespread proliferation of and interest in bracket pools that accompany the National Collegiate Athletic Association Division I Men’s Basketball Tournament have created a need to produce a set of predicted winners for each tournament game by people without expert knowledge of college basketball. Previous research has addressed bracket prediction to some degree, but not nearly on the level of the popular interest in the topic. This paper reviews relevant previous research, and then introduces a rating system for teams using game data from that season prior to the tournament. The ratings from this system are used within a novel, four-predictor probability model to produce sets of bracket predictions for each tournament from 2009 to 2014. This dual-proportion probability model is built around the constraint of two teams with a combined 100% probability of winning a given game. This paper also performs Monte Carlo simulation to investigate whether modifications are necessary from an expected value-based prediction system such as the one introduced in the paper, in order to have the maximum bracket score within a defined group. The findings are that selecting one high-probability “upset” team for one to three late rounds games is likely to outperform other strategies, including one with no modifications to the expected value, as long as the upset choice overlaps a large minority of competing brackets while leaving the bracket some distinguishing characteristics in late rounds.

Suggested Citation

  • Gupta Ajay Andrew, 2015. "A new approach to bracket prediction in the NCAA Men’s Basketball Tournament based on a dual-proportion likelihood," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 11(1), pages 53-67, March.
    • Handle: RePEc:bpj:jqsprt:v:11:y:2015:i:1:p:53-67:n:1
    DOI: 10.1515/jqas-2014-0047
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/jqas-2014-0047
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.1515/jqas-2014-0047?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
    ---><---

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

    References listed on IDEAS

    as
    1. West Brady T, 2006. "A Simple and Flexible Rating Method for Predicting Success in the NCAA Basketball Tournament," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 2(3), pages 1-16, July.
    2. Koenker, Roger & Bassett Jr., Gilbert W., 2010. "March Madness, Quantile Regression Bracketology, and the Hayek Hypothesis," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(1), pages 26-35.
    3. Metrick, Andrew, 1996. "March madness? Strategic behavior in NCAA basketball tournament betting pools," Journal of Economic Behavior & Organization, Elsevier, vol. 30(2), pages 159-172, August.
    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. Alessandro Chessa & Pierpaolo D’Urso & Livia Giovanni & Vincenzina Vitale & Alfonso Gebbia, 2023. "Complex networks for community detection of basketball players," Annals of Operations Research, Springer, vol. 325(1), pages 363-389, 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. Ludden Ian G. & Jacobson Sheldon H. & Khatibi Arash & King Douglas M., 2020. "Models for generating NCAA men’s basketball tournament bracket pools," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 16(1), pages 1-15, March.
    2. Khatibi, Arash & King, Douglas M. & Jacobson, Sheldon H., 2015. "Modeling the winning seed distribution of the NCAA Division I men׳s basketball tournament," Omega, Elsevier, vol. 50(C), pages 141-148.
    3. Stekler Herman O. & Klein Andrew, 2012. "Predicting the Outcomes of NCAA Basketball Championship Games," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 8(1), pages 1-10, March.
    4. Daniel C. Hickman & Andrew G. Meyer, 2017. "Does Athletic Success Influence Persistence At Higher Education Institutions? New Evidence Using Panel Data," Contemporary Economic Policy, Western Economic Association International, vol. 35(4), pages 658-676, October.
    5. repec:ebl:ecbull:v:4:y:2007:i:34:p:1-7 is not listed on IDEAS
    6. Phillip E. Pfeifer, 2016. "The promise of pick-the-winners contests for producing crowd probability forecasts," Theory and Decision, Springer, vol. 81(2), pages 255-278, August.
    7. Jaiho Chung & Joon Ho Hwang, 2010. "An Empirical Examination of the Parimutuel Sports Lottery Market versus the Bookmaker Market," Southern Economic Journal, John Wiley & Sons, vol. 76(4), pages 884-905, April.
    8. Karl Andrew T., 2012. "The Sensitivity of College Football Rankings to Several Modeling Choices," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 8(3), pages 1-44, October.
    9. Hoegh Andrew & Carzolio Marcos & Crandell Ian & Hu Xinran & Roberts Lucas & Song Yuhyun & Leman Scotland C., 2015. "Nearest-neighbor matchup effects: accounting for team matchups for predicting March Madness," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 11(1), pages 29-37, March.
    10. West Brady T & Lamsal Madhur, 2008. "A New Application of Linear Modeling in the Prediction of College Football Bowl Outcomes and the Development of Team Ratings," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 4(3), pages 1-21, July.
    11. Marco Ottaviani & Peter Norman Sorensen, 2010. "Noise, Information, and the Favorite-Longshot Bias in Parimutuel Predictions," American Economic Journal: Microeconomics, American Economic Association, vol. 2(1), pages 58-85, February.
    12. Daniel F. Stone & Jeremy Arkes, 2018. "March Madness? Underreaction To Hot And Cold Hands In Ncaa Basketball," Economic Inquiry, Western Economic Association International, vol. 56(3), pages 1724-1747, July.
    13. Jacobson, Sheldon H. & Nikolaev, Alexander G. & King, Douglas M. & Lee, Adrian J., 2011. "Seed distributions for the NCAA men's basketball tournament," Omega, Elsevier, vol. 39(6), pages 719-724, December.
    14. Lopez Michael J. & Matthews Gregory J., 2015. "Building an NCAA men’s basketball predictive model and quantifying its success," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 11(1), pages 5-12, March.
    15. Galvao Jr., Antonio F., 2011. "Quantile regression for dynamic panel data with fixed effects," Journal of Econometrics, Elsevier, vol. 164(1), pages 142-157, September.
    16. Bryan Clair & David Letscher, 2007. "Optimal Strategies for Sports Betting Pools," Operations Research, INFORMS, vol. 55(6), pages 1163-1177, December.
    17. David Bergman & Jason Imbrogno, 2017. "Surviving a National Football League Survivor Pool," Operations Research, INFORMS, vol. 65(5), pages 1343-1354, October.
    18. B. Jay Coleman & Allen K. Lynch, 2001. "Identifying the NCAA Tournament “Dance Card”," Interfaces, INFORMS, vol. 31(3), pages 76-86, June.
    19. Komunjer, Ivana, 2013. "Quantile Prediction," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 961-994, Elsevier.
    20. Coleman Jay & Lynch Allen K, 2009. "NCAA Tournament Games: The Real Nitty-Gritty," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 5(3), pages 1-27, July.
    21. Ottaviani, Marco & Sørensen, Peter Norman, 2003. "Late Informed Betting and the Favourite-Longshot Bias," CEPR Discussion Papers 4092, C.E.P.R. Discussion Papers.

    More about this item

    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:bpj:jqsprt:v:11:y:2015:i:1:p:53-67:n:1. 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: Peter Golla (email available below). General contact details of provider: https://www.degruyter.com .

    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.