IDEAS home Printed from https://ideas.repec.org/a/bpj/jqsprt/v7y2011i2n11.html
   My bibliography  Save this article

Monte Carlo Simulation for High School Football Playoff Seed Projection

Author

Listed:
  • Pasteur R. Drew

    (The College of Wooster)

  • Janning Michael C.

    (The College of Wooster)

Abstract

In Ohio high school football, playoff teams are selected and seeded using an objective point system. Roughly one-fourth of the state's teams earn playoff berths, and higher seeds host first-round games. Even in the final week of the season, a team's playoff chances can depend on the outcomes of dozens of other games, making direct computation of playoff probabilities impractical. To make playoff-related predictions, we first estimate win probabilities for all remaining regular-season games by applying a predictive ranking algorithm, then repeatedly simulate the remainder of the regular season. Using the aggregate results, we predict the playoff qualifiers and seeds, and also estimate conditional probabilities (based on the number of future wins) that particular teams earn a berth or a home game. In tracking the results of this model over two seasons, we find that modeling future games substantially increases the accuracy of seed predictions, but adds far less value in predicting the qualifying teams. This phenomenon may be related to the specificity of seed prediction, as compared to the more general nature of predicting a group of teams likely to qualify. That is, the additional information is most useful when making more specific predictions.

Suggested Citation

  • Pasteur R. Drew & Janning Michael C., 2011. "Monte Carlo Simulation for High School Football Playoff Seed Projection," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 7(2), pages 1-10, May.
    • Handle: RePEc:bpj:jqsprt:v:7:y:2011:i:2:n:11
    DOI: 10.2202/1559-0410.1330
    as

    Download full text from publisher

    File URL: https://doi.org/10.2202/1559-0410.1330
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.2202/1559-0410.1330?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. Newton Paul K & Aslam Kamran, 2009. "Monte Carlo Tennis: A Stochastic Markov Chain Model," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 5(3), pages 1-44, July.
    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. Pettigrew Stephen, 2014. "How the West will be won: using Monte Carlo simulations to estimate the effects of NHL realignment," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 10(3), pages 1-11, September.

    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. Bizzozero, Paolo & Flepp, Raphael & Franck, Egon, 2016. "The importance of suspense and surprise in entertainment demand: Evidence from Wimbledon," Journal of Economic Behavior & Organization, Elsevier, vol. 130(C), pages 47-63.
    2. Pettigrew Stephen, 2014. "How the West will be won: using Monte Carlo simulations to estimate the effects of NHL realignment," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 10(3), pages 1-11, September.
    3. Noubary Reza D. & Coles Drue, 2011. "Rule of Tangent for Win-By-Two Games," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 7(4), pages 1-18, October.
    4. Goldner Keith, 2012. "A Markov Model of Football: Using Stochastic Processes to Model a Football Drive," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 8(1), pages 1-18, March.
    5. Gonzalez-Cabrera Ivan & Herrera Diego Dario & González Diego Luis, 2020. "Generalized model for scores in volleyball matches," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 16(1), pages 41-55, March.
    6. Heiny Erik L. & Heiny Robert Lowell, 2014. "Stochastic model of the 2012 PGA Tour season," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 10(4), pages 1-13, December.
    7. Sung, Ming-Chien & McDonald, David C.J. & Johnson, Johnnie E.V. & Tai, Chung-Ching & Cheah, Eng-Tuck, 2019. "Improving prediction market forecasts by detecting and correcting possible over-reaction to price movements," European Journal of Operational Research, Elsevier, vol. 272(1), pages 389-405.
    8. Zhou, Yunjing & Zong, Shouxin & Cao, Run & Gómez, Miguel-Ángel & Chen, Chuqi & Cui, Yixiong, 2023. "Using network science to analyze tennis stroke patterns," Chaos, Solitons & Fractals, Elsevier, vol. 170(C).
    9. Galeano, Javier & Gómez, Miguel-Ángel & Rivas, Fernando & Buldú, Javier M., 2022. "Using Markov chains to identify player’s performance in badminton," Chaos, Solitons & Fractals, Elsevier, vol. 165(P2).
    10. Marc Garnica-Caparrós & Daniel Memmert & Fabian Wunderlich, 2022. "Artificial data in sports forecasting: a simulation framework for analysing predictive models in sports," Information Systems and e-Business Management, Springer, vol. 20(3), pages 551-580, September.
    11. Fabian Wunderlich & Daniel Memmert, 2018. "The Betting Odds Rating System: Using soccer forecasts to forecast soccer," PLOS ONE, Public Library of Science, vol. 13(6), pages 1-18, June.
    12. Chan Timothy C.Y. & Singal Raghav, 2018. "A Bayesian regression approach to handicapping tennis players based on a rating system," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 14(3), pages 131-141, September.

    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:7:y:2011:i:2:n:11. 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.