IDEAS home Printed from https://ideas.repec.org/a/inm/orited/v21y2020i1p57-60.html
   My bibliography  Save this article

Case Article—Converting NFL Point Spreads into Probabilities: A Case Study for Teaching Business Analytics

Author

Listed:
  • Eric Huggins

    (School of Business Administration, Fort Lewis College, Durango, Colorado 81301)

  • Matt Bailey

    (Freeman College of Management, Bucknell University, Lewisburg, Pennsylvania 17837)

  • Ivan Guardiola

    (School of Business Administration, Fort Lewis College, Durango, Colorado 81301)

Abstract

In this case study, students determine the relationship between point spreads and the probability of winning a game using data from the National Football League (NFL); although the data comes from the NFL, the models and insights are accessible to students who are unfamiliar with football. They model the relationship first with a linear fit and then with a logistic curve. The analysis requires a combination of several key Microsoft Excel functions, including PivotTables, trendlines, and Solver. The case is designed to be tailored to the needs of the instructor, the students, and the course—the basic case can be relatively short but several additional options add depth and reinforcement; similarly, it can be assigned as an unstructured or highly structured assignment. The case itself introduces the idea that with enough data on previous point spreads and game results, we can calculate the empirical probability that a team favored by a given point spread will win the game. The case Teaching Note provides detailed instructions for every step of the case, with hints for both instructors and students. This case article discusses the positive pedagogical aspects.

Suggested Citation

  • Eric Huggins & Matt Bailey & Ivan Guardiola, 2020. "Case Article—Converting NFL Point Spreads into Probabilities: A Case Study for Teaching Business Analytics," INFORMS Transactions on Education, INFORMS, vol. 21(1), pages 57-60, September.
  • Handle: RePEc:inm:orited:v:21:y:2020:i:1:p:57-60
    DOI: 10.1287/ited.2019.0230ca
    as

    Download full text from publisher

    File URL: https://doi.org/10.1287/ited.2019.0230ca
    Download Restriction: no

    File URL: https://libkey.io/10.1287/ited.2019.0230ca?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
    ---><---

    Citations

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


    Cited by:

    1. Michael Brusco, 2022. "Logistic Regression via Excel Spreadsheets: Mechanics, Model Selection, and Relative Predictor Importance," INFORMS Transactions on Education, INFORMS, vol. 23(1), pages 1-11, September.

    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:inm:orited:v:21:y:2020:i:1:p:57-60. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    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.