Policy Changes in Major League Baseball: Improved Agent Behavior and Ancillary Productivity Outcomes

Mills, Brian M.

doi:10.1111/ecin.12396

Cited by 11 publications

(8 citation statements)

References 49 publications

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“…As mentioned in Section 3.1, Roegelle (2014), Mills (2017a), and Mills (2017b have documented year-to-year changes in umpires' strike zone enforcement ever since Major League Baseball began reviewing and grading umpires' decisions in 2009. In other words, umpire tendencies are nonstationary across seasons and we cannot reasonably expect Models 4 and 5, which attempt to identify umpire-specific player effects, to forecast future umpire decisions particularly well.…”

Section: A Model Comparison With Cross-validationmentioning

confidence: 99%

“…The fact that Models 4 and 5 have worse out-of-sample performance, despite having very good in-sample performance is a clear indication that these two over-parametrized models have overfit the data. One could argue, however, that comparing predictive performance on 2015 data is not the best means of diagnosing overfitting Roegelle (2014),Mills (2017a), andMills (2017b). have documented year-to-year changes in umpires' strike zone enforcement ever since Major League Baseball began reviewing and grading umpires' decisions in 2009.…”

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confidence: 99%

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A hierarchical Bayesian model of pitch framing

Deshpande

Wyner

2017

Journal of Quantitative Analysis in Sports

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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 called as a strike). Especially in the last three 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 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 to 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.

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Section: A Model Comparison With Cross-validationmentioning

confidence: 99%

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confidence: 99%

A hierarchical Bayesian model of pitch framing

Deshpande

Wyner

2017

Journal of Quantitative Analysis in Sports

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“…Even with more advanced technology, the gains have been small. Mills (2017b) also demonstrates that league-mandated changes regarding the enforcement of the strike zone had a significant impact on offense. This analysis shows that umpires were highly responsive to principal edicts, and thus, offense changes could have been achieved through strike zone manipulation with pre-QuesTec oversight.…”

Section: Discussionmentioning

confidence: 96%

“…MLB was facing scrutiny regarding performance-enhancing drugs as the chief contributor to the boost in offense during this era. Mills (2017b) demonstrates that strike zone manipulation after the QuesTec era by MLB was correlated with reductions in offense; perhaps QuesTec was implemented with similar intentions. Second, baseball games were growing longer, and MLB openly discussed methods for shortening the games.…”

Section: Impact Of Questec Monitoring On the Play Of The Gamementioning

confidence: 95%

Monitoring and Employee Shirking: Evidence From MLB Umpires

Bradbury

2018

Journal of Sports Economics

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Neoclassical principal–agent theory predicts stricter monitoring should reduce employee shirking; however, recent analyses indicate social aspects of principal–agent relationships may result in “crowding out” of disciplinary effects. Asymmetric implementation of an automated pitch-tracking system in baseball allows for the comparison of monitored and unmonitored umpires to identify shirking in light of incentives. Estimates identify some reduced shirking with monitoring; however, overall, umpires appeared to be quite sensitive to league directives absent technological monitoring. Extreme sensitivity to MLB mandates when unmonitored by the new technology indicates that preexisting monitoring (which included human oversight and efficiency wages) was effective at limiting shirking.

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“…Other studies model the probability that a called pitch is called a strike as a function of its location. The most successful of these latter studies, for example, Mills (2014Mills ( , 2016aMills ( , 2016b, Tainsky, Mills and Winfree (2015), and Deshpande and Wyner (2017), are based on a semiparametric generalized additive model (GAM) of the log odds that the ith pitch is called a strike of form log P (z i = 1)…”

Section: Introductionmentioning

confidence: 99%

Outline analyses of the called strike zone in Major League Baseball

Zimmerman¹,

Tang²,

Huang³

2019

Ann. Appl. Stat.

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We extend statistical shape analytic methods known as outline analysis for application to the strike zone, a central feature of the game of baseball. Although the strike zone is rigorously defined by Major League Baseball's official rules, umpires make mistakes in calling pitches as strikes (and balls) and may even adhere to a strike zone somewhat different than that prescribed by the rule book. Our methods yield inference on geometric attributes (centroid, dimensions, orientation and shape) of this "called strike zone" (CSZ) and on the effects that years, umpires, player attributes, game situation factors and their interactions have on those attributes. The methodology consists of first using kernel discriminant analysis to determine a noisy outline representing the CSZ corresponding to each factor combination, then fitting existing elliptic Fourier and new generalized superelliptic models for closed curves to that outline and finally analyzing the fitted model coefficients using standard methods of regression analysis, factorial analysis of variance and variance component estimation. We apply these methods to PITCHf/x data comprising more than three million called pitches from the 2008-2016 Major League Baseball seasons to address numerous questions about the CSZ. We find that all geometric attributes of the CSZ, except its size, became significantly more like those of the rule-book strike zone from 2008-2016 and that several player attribute/game situation factors had statistically and practically significant effects on many of them. We also establish that the variation in the horizontal center, width and area of an individual umpire's CSZ from pitch to pitch is smaller than their variation among CSZs from different umpires.

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Policy Changes in Major League Baseball: Improved Agent Behavior and Ancillary Productivity Outcomes

Cited by 11 publications

References 49 publications

A hierarchical Bayesian model of pitch framing

A hierarchical Bayesian model of pitch framing

Monitoring and Employee Shirking: Evidence From MLB Umpires

Outline analyses of the called strike zone in Major League Baseball

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