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A hierarchical Bayesian model of pitch framing

Author

Listed:
  • Deshpande Sameer K.

    (The Wharton School, University of Pennsylvania – Statistics, 434 Jon M. Huntsman Hall, 3730 Walnut St., Philadelphia, PA 19104, USA)

  • Wyner Abraham

    (University of Pennsylvania, Philadelphia, PA 19104-6243, USA)

Abstract

Since the advent of high-resolution pitch tracking data (PITCHf/x), many in the sabermetrics community have attempted to quantify a Major League Baseball catcher’s ability to “frame” a pitch (i.e. increase the chance that a pitch is a called as a strike). Especially in the last 3 years, there has been an explosion of interest in the “art of pitch framing” in the popular press as well as signs that teams are considering framing when making roster decisions. We introduce a Bayesian hierarchical model to estimate each umpire’s probability of calling a strike, adjusting for the pitch participants, pitch location, and contextual information like the count. Using our model, we can estimate each catcher’s effect on an umpire’s chance of calling a strike. We are then able translate these estimated effects into average runs saved across a season. We also introduce a new metric, analogous to Jensen, Shirley, and Wyner’s Spatially Aggregate Fielding Evaluation metric, which provides a more honest assessment of the impact of framing.

Suggested Citation

  • Deshpande Sameer K. & Wyner Abraham, 2017. "A hierarchical Bayesian model of pitch framing," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 13(3), pages 95-112, September.
    • Handle: RePEc:bpj:jqsprt:v:13:y:2017:i:3:p:95-112:n:1
    DOI: 10.1515/jqas-2017-0027
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    References listed on IDEAS

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    1. Albert Jim, 2010. "Using the Count to Measure Pitching Performance," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 6(4), pages 1-30, October.
    2. Daniel L. Chen & Tobias J. Moskowitz & Kelly Shue, 2016. "Decision Making Under the Gambler’s Fallacy: Evidence from Asylum Judges, Loan Officers, and Baseball Umpires," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(3), pages 1181-1242.
    3. Brian M. Mills, 2017. "Policy Changes In Major League Baseball: Improved Agent Behavior And Ancillary Productivity Outcomes," Economic Inquiry, Western Economic Association International, vol. 55(2), pages 1104-1118, April.
    4. Scott Tainsky & Brian M. Mills & Jason A. Winfree, 2015. "Further Examination of Potential Discrimination Among MLB Umpires," Journal of Sports Economics, , vol. 16(4), pages 353-374, May.
    5. Jerry W. Kim & Brayden G King, 2014. "Seeing Stars: Matthew Effects and Status Bias in Major League Baseball Umpiring," Management Science, INFORMS, vol. 60(11), pages 2619-2644, November.
    6. Mills, Brian M., 2017. "Technological innovations in monitoring and evaluation: Evidence of performance impacts among Major League Baseball umpires," Labour Economics, Elsevier, vol. 46(C), pages 189-199.
    7. Chen, Daniel L. & Moskowitz, Tobias J. & Shue, Kelly, 2016. "Decision-Making Under the Gambler’s Fallacy: Evidence From Asylum Courts, Loan Officers, and Baseball Umpires," IAST Working Papers 16-43, Institute for Advanced Study in Toulouse (IAST).
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    Cited by:

    1. Jyh-How Huang & Yu-Chia Hsu, 2021. "A Multidisciplinary Perspective on Publicly Available Sports Data in the Era of Big Data: A Scoping Review of the Literature on Major League Baseball," SAGE Open, , vol. 11(4), pages 21582440211, November.

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