Lead distance under a pickoff limit in Major League Baseball: A sequential game model

Fuente: arXiv
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Main Authors: Powers, Scott, Ramani, Sivaramakrishnan, Hahn, Jacob, Schaefer, Andrew J.
Format: Preprint
Published: 2026
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author Powers, Scott
Ramani, Sivaramakrishnan
Hahn, Jacob
Schaefer, Andrew J.
author_facet Powers, Scott
Ramani, Sivaramakrishnan
Hahn, Jacob
Schaefer, Andrew J.
contents Major League Baseball (MLB) recently limited pitchers to three pickoff attempts, creating a cat-and-mouse game between pitcher and runner. Each failed attempt adds pressure on the pitcher to avoid using another, and the runner can intensify this pressure by extending their leadoff toward the next base. We model this dynamic as a two-player zero-sum sequential game in which the runner first chooses a lead distance, and then the pitcher chooses whether to attempt a pickoff. We establish optimality characterizations for the game and present variants of value iteration and policy iteration to solve the game. Using lead distance data, we estimate generalized linear mixed-effects models for pickoff and stolen base outcome probabilities given lead distance, context, and player skill. We compute the game-theoretic equilibria under the two-player model, as well as the optimal runner policy under a simplified one-player Markov decision process (MDP) model. In the one-player setting, our results establish an actionable rule of thumb: the Two-Foot Rule, which recommends that a runner increase their lead by two feet after each pickoff attempt.
format Preprint
id arxiv_https___arxiv_org_abs_2601_15608
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Lead distance under a pickoff limit in Major League Baseball: A sequential game model
Powers, Scott
Ramani, Sivaramakrishnan
Hahn, Jacob
Schaefer, Andrew J.
Optimization and Control
Applications
Major League Baseball (MLB) recently limited pitchers to three pickoff attempts, creating a cat-and-mouse game between pitcher and runner. Each failed attempt adds pressure on the pitcher to avoid using another, and the runner can intensify this pressure by extending their leadoff toward the next base. We model this dynamic as a two-player zero-sum sequential game in which the runner first chooses a lead distance, and then the pitcher chooses whether to attempt a pickoff. We establish optimality characterizations for the game and present variants of value iteration and policy iteration to solve the game. Using lead distance data, we estimate generalized linear mixed-effects models for pickoff and stolen base outcome probabilities given lead distance, context, and player skill. We compute the game-theoretic equilibria under the two-player model, as well as the optimal runner policy under a simplified one-player Markov decision process (MDP) model. In the one-player setting, our results establish an actionable rule of thumb: the Two-Foot Rule, which recommends that a runner increase their lead by two feet after each pickoff attempt.
title Lead distance under a pickoff limit in Major League Baseball: A sequential game model
topic Optimization and Control
Applications
url https://arxiv.org/abs/2601.15608