IDEAS home Printed from https://ideas.repec.org/a/bpj/jqsprt/v6y2010i3n8.html
   My bibliography  Save this article

A Point-Mass Mixture Random Effects Model for Pitching Metrics

Author

Listed:
  • Piette James

    (University of Pennsylvania)

  • Braunstein Alexander

    (Google, Inc.)

  • McShane Blakeley B

    (Kellogg School of Management, Northwestern University)

  • Jensen Shane T.

    (University of Pennsylvania)

Abstract

A plethora of statistics have been proposed to measure the effectiveness of pitchers in Major League Baseball. While many of these are quite traditional (e.g., ERA, wins), some have gained currency only recently (e.g., WHIP, K/BB). Some of these metrics may have predictive power, but it is unclear which are the most reliable or consistent. We address this question by constructing a Bayesian random effects model that incorporates a point mass mixture and fitting it to data on twenty metrics spanning approximately 2,500 players and 35 years. Our model identifies FIP, HR/9, ERA, and BB/9 as the highest signal metrics for starters and GB%, FB%, and K/9 as the highest signal metrics for relievers. In general, the metrics identified by our model are independent of team defense. Our procedure also provides a relative ranking of metrics separately by starters and relievers and shows that these rankings differ quite substantially between them. Our methodology is compared to a Lasso-based procedure and is internally validated by detailed case studies.

Suggested Citation

  • Piette James & Braunstein Alexander & McShane Blakeley B & Jensen Shane T., 2010. "A Point-Mass Mixture Random Effects Model for Pitching Metrics," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 6(3), pages 1-17, July.
  • Handle: RePEc:bpj:jqsprt:v:6:y:2010:i:3:n:8
    as

    Download full text from publisher

    File URL: https://www.degruyter.com/view/j/jqas.2010.6.3/jqas.2010.6.3.1237/jqas.2010.6.3.1237.xml?format=INT
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.

    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. Albert James, 2006. "Pitching Statistics, Talent and Luck, and the Best Strikeout Seasons of All-Time," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 2(1), pages 1-32, January.
    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. Albert Jim, 2016. "Improved component predictions of batting and pitching measures," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 12(2), pages 73-85, 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:6:y:2010:i:3:n:8. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Peter Golla). General contact details of provider: https://www.degruyter.com .

    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 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.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.