On the Decision to Take a Pitch

Bickel, J. Eric

doi:10.1287/deca.1090.0145

Cited by 8 publications

(3 citation statements)

References 4 publications

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“…Consider the fact that a batter in possession of detailed knowledge of a pitcher's processes for determining pitches to throw would have a large advantage for exploiting that pitcher to get on base [Stallings, Bennett and American Baseball Coaches Association (2003)]. Pitchers apparently reveal an enormous amount of information regarding their behaviour through their historical game data [Bickel (2009)]. However, making effective use of this data is challenging.…”

Section: -Casey Stengelmentioning

confidence: 99%

MONEYBaRL: Exploiting pitcher decision-making using Reinforcement Learning

Sidhu

Caffo

2014

Ann. Appl. Stat.

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This manuscript uses machine learning techniques to exploit baseball pitchers' decision making, so-called "Baseball IQ," by modeling the at-bat information, pitch selection and counts, as a Markov Decision Process (MDP). Each state of the MDP models the pitcher's current pitch selection in a Markovian fashion, conditional on the information immediately prior to making the current pitch. This includes the count prior to the previous pitch, his ensuing pitch selection, the batter's ensuing action and the result of the pitch.The necessary Markovian probabilities can be estimated by the relevant observed conditional proportions in MLB pitch-by-pitch game data. These probabilities could be pitcher-specific, using only the data from one pitcher, or general, using the data from a collection of pitchers.Optimal batting strategies against these estimated conditional distributions of pitch selection can be ascertained by Value Iteration. Optimal batting strategies against a pitcher-specific conditional distribution can be contrasted to those calculated from the general conditional distributions associated with a collection of pitchers.In this manuscript, a single season of MLB data is used to calculate the conditional distributions to find optimal pitcher-specific and general (against a collection of pitchers) batting strategies. These strategies are subsequently evaluated by conditional distributions calculated from a different season for the same pitchers. Thus, the batting strategies are conceptually tested via a collection of simulated games, a "mock season," governed by distributions not used to create the strategies. (Simulation is not needed, as exact calculations are available.)Instances where the pitcher-specific batting strategy outperforms the general batting strategy suggests that the pitcher is exploitableknowledge of the conditional distributions of their pitch-making decision process in a different season yielded a strategy that worked better in a new season than a general batting strategy built on a

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Section: -Casey Stengelmentioning

confidence: 99%

MONEYBaRL: Exploiting pitcher decision-making using Reinforcement Learning

Sidhu

Caffo

2014

Ann. Appl. Stat.

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“…There are numerous papers on in-game strategy alone, with applications in baseball (Bickel, 2009), soccer (Brimberg and Hurley, 1999), football (Carter and Machol, 1978), cricket (Clarke, 1988), and curling (Willoughby and Kostuk, 2012), to cite just a few relevant examples.…”

Section: Introductionmentioning

confidence: 99%

When to challenge a call in tennis: A Markov decision process approach

Nadimpalli¹,

Hasenbein

2013

Journal of Quantitative Analysis in Sports

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In this paper, we develop a Markov decision process (MDP) model to determine when a player should challenge a line call in a game of tennis, if the objective is to maximize the probability of winning the game. The parameters in the model include the relative strength of the players and the fallibility of the officials. The player's decision depends on the the current score, the number of challenges remaining, the outcome of a successful challenge, and his confidence that the line call is incorrect. The model developed is a multi-chain MDP operating under the long-run average cost criterion. We also performed extensive numerical studies when the player has one challenge remaining, varying the player strengths and the fallibility levels. These studies imply some general intuitive challenge strategies but also exhibit unusual strategic behavior in some game states. For example, in some states it is not optimal for very weak or very strong players to challenge calls. Furthermore, we demonstrate that the challenge decision is not "unimodal" as a function of the player's strength, i.e., there may be multiple decision thresholds with respect to this parameter.

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“…A prior paper in Decision Analysis by this research team is Montiel and Bickel (2012) on a simulation procedure that can create a collection of possible joint probability distributions to match known probabilistic information Bickel's other prior papers on probabilities in Decision Analysis include Bickel (2007Bickel ( , 2010) on probability scoring rules. He has also published in Decision Analysis on using baseball examples in teaching (Bickel 2009), optimal sequential exploration (Bickel and Smith 2006), the value of information (Bickel 2008), and corporate risk aversion (Bickel 2006). Bickel also serves the journal as an associate editor.…”

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confidence: 99%

From the Editors: Probability Approximations, Anti-Terrorism Strategy, and Bull's-Eye Display for Performance Feedback

Sarin

Keller

2013

Decision Analysis

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This “From the Editors” column is coauthored by new Editor-in-Chief Rakesh K. Sarin and former Editor-in-Chief L. Robin Keller. Our first two articles are on techniques for approximating probability distributions. The first article, by Robert K. Hammond and J. Eric Bickel is on “Reexamining Discrete Approximations to Continuous Distributions.” Next, Luis V. Montiel and J. Eric Bickel present a method for “Approximating Joint Probability Distributions Given Partial Information.” The next two articles present methods for analyzing terrorist threats or measures for terrorist protection. Kevin Ni, Daniel Faissol, Thomas Edmunds, and Richard Wheeler present a new method for “Exploitation of Ambiguous Cues to Infer Terrorist Activity.” The next article is on “Passenger Profiling and Screening for Aviation Security in the Presence of Strategic Attackers” by Huseyin Cavusoglu, Young Kwark, Bin Mai, and Srinivasan Raghunathan. Our final paper is on “WholeSoldier Performance Appraisal to Support Mentoring and Personnel Decisions,” by Robert A. Dees, Scott T. Nestler, and Robert Kewley.

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On the Decision to Take a Pitch

Cited by 8 publications

References 4 publications

MONEYBaRL: Exploiting pitcher decision-making using Reinforcement Learning

MONEYBaRL: Exploiting pitcher decision-making using Reinforcement Learning

When to challenge a call in tennis: A Markov decision process approach

From the Editors: Probability Approximations, Anti-Terrorism Strategy, and Bull's-Eye Display for Performance Feedback

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