Date of Original Version

4-2011

Type

Thesis

Rights Management

All Rights Reserved

Abstract or Description

An analysis of the pitcher's intent in baseball should ideally depend upon information given by the catcher before the pitch is thrown. Expert judgment has already identified the importance of this factor (e.g. the pitcher is "missing his spots" when unsuccessful in hitting the target). We describe the underpinnings of a automated video analysis system that uses semi-supervised learning methods to identify the catcher position - specifically, the catcher's glove position. The analysis begins with video of a single pitch, supervised by a human controller; this information is then incorporated into one of a selection of learning algorithms and applied to subsequent pitches with minimal involvement from the controller. This is designed to be the first step in creating a public database of pitch intent, to be coupled with existing sources of pitch physics, so that analysts may better evaluate pitcher performance.

Comments

Department of Statistics

Andrew Thomas, advisor

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