IDEAS home Printed from https://ideas.repec.org/a/bpj/jqsprt/v7y2011i3n19.html
   My bibliography  Save this article

Comparing Hall of Fame Baseball Players Using Most Valuable Player Ranks

Author

Listed:
  • Kvam Paul H

    (Georgia Institute of Technololgy)

Abstract

We propose a rank-based statistical procedure for comparing performances of top major league baseball players who performed in different eras. The model is based on using the player ranks from voting results for the most valuable player awards in the American and National Leagues. The current voting procedure has remained the same since 1932, so the analysis regards only data for players whose career blossomed after that time. Because the analysis is based on quantiles, its basis is nonparametric and relies on a simple link function. Results are stratified by fielding position, and we compare 73 Hall of Fame players up to 2010. We also consider the players on the 2011 Hall of Fame ballot as well as other potential Hall of Fame candidates. The analysis is based on the method of maximum likelihood, and results are illustrated graphically.

Suggested Citation

  • Kvam Paul H, 2011. "Comparing Hall of Fame Baseball Players Using Most Valuable Player Ranks," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 7(3), pages 1-22, July.
  • Handle: RePEc:bpj:jqsprt:v:7:y:2011:i:3:n:19
    DOI: 10.2202/1559-0410.1337
    as

    Download full text from publisher

    File URL: https://doi.org/10.2202/1559-0410.1337
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.

    File URL: https://libkey.io/10.2202/1559-0410.1337?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. Phillips Andrew J. K., 2014. "Uncovering Formula One driver performances from 1950 to 2013 by adjusting for team and competition effects," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 10(2), pages 1-18, June.
    2. Vock David Michael & Vock Laura Frances Boehm, 2018. "Estimating the effect of plate discipline using a causal inference framework: an application of the G-computation algorithm," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 14(2), pages 37-56, June.

    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:3:n:19. 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.