IDEAS home Printed from https://ideas.repec.org/a/bpj/jqsprt/v8y2012i3n7.html
   My bibliography  Save this article

Estimating Fielding Ability in Baseball Players Over Time

Author

Listed:
  • Piette James

    (University of Pennsylvania)

  • Jensen Shane T.

    (University of Pennsylvania)

Abstract

Quantitative evaluation of fielding ability in baseball has been an ongoing challenge for statisticians. Detailed recording of ball-in-play data in recent years has spurred the development of sophisticated fielding models. Foremost among these approaches, Jensen et al. (2009) used a hierarchical Bayesian model to estimate spatial fielding curves for individual players. These previous efforts have not addressed evolution in a player’s fielding ability over time. We expand the work of Jensen et al. (2009) to model the fielding ability of individual players over multiple seasons. Several different models are implemented and compared via posterior predictive validation on hold-out data. Among our choices, we find that a model which imposes shrinkage towards an age-specific average gives the best performance. Our temporal models allow us to delineate the performance of a fielder on a season-to-season basis versus their entire career.

Suggested Citation

  • Piette James & Jensen Shane T., 2012. "Estimating Fielding Ability in Baseball Players Over Time," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 8(3), pages 1-36, October.
  • Handle: RePEc:bpj:jqsprt:v:8:y:2012:i:3:n:7
    DOI: 10.1515/1559-0410.1463
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/1559-0410.1463
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.

    File URL: https://libkey.io/10.1515/1559-0410.1463?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. Kaplan David, 2008. "Univariate and Multivariate Autoregressive Time Series Models of Offensive Baseball Performance: 1901-2005," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 4(3), pages 1-23, July.
    2. Null Brad, 2009. "Modeling Baseball Player Ability with a Nested Dirichlet Distribution," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 5(2), pages 1-38, May.
    3. Kalist David E & Spurr Stephen J, 2006. "Baseball Errors," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 2(4), pages 1-22, October.
    4. Reich, Brian J. & Hodges, James S. & Carlin, Bradley P. & Reich, Adam M., 2006. "A Spatial Analysis of Basketball Shot Chart Data," The American Statistician, American Statistical Association, vol. 60, pages 3-12, February.
    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. Yurko Ronald & Ventura Samuel & Horowitz Maksim, 2019. "nflWAR: a reproducible method for offensive player evaluation in football," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 15(3), pages 163-183, September.
    2. 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.

    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. Thomas Dohmen & Jan Sauermann, 2016. "Referee Bias," Journal of Economic Surveys, Wiley Blackwell, vol. 30(4), pages 679-695, September.
    2. McShane Blakeley B. & Braunstein Alexander & Piette James & Jensen Shane T., 2011. "A Hierarchical Bayesian Variable Selection Approach to Major League Baseball Hitting Metrics," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 7(4), pages 1-26, October.
    3. Gerber Eric A. E. & Craig Bruce A., 2021. "A mixed effects multinomial logistic-normal model for forecasting baseball performance," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 17(3), pages 221-239, September.
    4. Baumer Ben S. & Piette James & Null Brad, 2012. "Parsing the Relationship between Baserunning and Batting Abilities within Lineups," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 8(2), pages 1-19, June.
    5. Seong W. Kim & Sabina Shahin & Hon Keung Tony Ng & Jinheum Kim, 2021. "Binary segmentation procedures using the bivariate binomial distribution for detecting streakiness in sports data," Computational Statistics, Springer, vol. 36(3), pages 1821-1843, 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:8:y:2012:i:3:n:7. 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.