IDEAS home Printed from https://ideas.repec.org/a/bpj/jqsprt/v5y2009i1n6.html
   My bibliography  Save this article

Predicting Baseball Hall of Fame Membership using a Radial Basis Function Network

Author

Listed:
  • Smith Lloyd

    (Missouri State University - Springfield)

  • Downey James

    (University of Central Arkansas)

Abstract

This paper describes an objective way of predicting the likelihood of major league baseball players being elected to the Hall of Fame by members of the Baseball Writers' Association of America. A radial basis function (RBF) network is used to build separate machine learning models for pitchers and non-pitchers. These models use simple player statistics such as number of wins and earned run average for pitchers and number of hits and home runs for non-pitchers. The models are trained on data representing players who played for at least 10 years and who retired between the years 1950 and 2002. In cross-validation tests, the models correctly identified 21 of 24 Hall of Fame pitchers, with 3 false positives, and 38 of 45 non-pitchers, with 7 false positives. When run over data representing active and recently retired players who played for at least 10 years, the models rate 12 pitchers and 22 non-pitchers to have approximately a 40% or better chance of election to the Hall of Fame.

Suggested Citation

  • Smith Lloyd & Downey James, 2009. "Predicting Baseball Hall of Fame Membership using a Radial Basis Function Network," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 5(1), pages 1-21, January.
    • Handle: RePEc:bpj:jqsprt:v:5:y:2009:i:1:n:6
    DOI: 10.2202/1559-0410.1157
    as

    Download full text from publisher

    File URL: https://doi.org/10.2202/1559-0410.1157
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.2202/1559-0410.1157?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.

    Citations

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


    Cited by:

    1. Hamrick Jeff & Rasp John, 2011. "Using Local Correlation to Explain Success in Baseball," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 7(4), pages 1-29, October.
    2. Mills Brian M. & Salaga Steven, 2011. "Using Tree Ensembles to Analyze National Baseball Hall of Fame Voting Patterns: An Application to Discrimination in BBWAA Voting," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 7(4), pages 1-32, October.

    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:5:y:2009:i:1:n:6. 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: 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.