Baseball Simulation

UNIVERSITY OF CALIFORNIA
Los Angeles

A Player Based Approach to Baseball Simulation

A dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Statistics

by

Adam Philip Sugano

2008

© Copyright by

Adam Philip Sugano

2008

The dissertation of Adam Philip Sugano is approved.

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Jan de Leeuw

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Rick Paik Schoenberg

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Hal Stern

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Mark Hansen, Committee Co-Chair

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Don Morrison, Committee Co-Chair

University of California, Los Angeles
2008

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To my favorite statistician and my Dad, Dr. David S. Sugano, with love and admiration…

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TABLE OF CONTENTS
1

Introduction................................................................................................ 1

2

Baseball within a Markov Chain Framework......................................... 5
2.1

Markov Chain Properties................................................................. 6

2.2

Previous Work Modeling Baseball via Markov a Markov Chain.... 8

2.3

The Transition Matrix...................................................................... 9
2.3.1
2.3.2

3

Transition Constraints.......................................................... 13

2.3.3
2.4

Run Potentials...................................................................... 12

Finalized Transition Matrices.............................................. 14

Advantages of Simulation Models................................................... 23

Situational Factors Affecting Transition Probabilities.......................... 25
3.1

Bias and Evolvement of Baseball Statistics.................................... 26

3.2

Measuring Chance vs. Ability.........................................................