Research Mentor(s)
Hutchinson, Brian
Description
The 162 game long Major League Baseball season provides ample time for a player’s performance to vary and trend in different directions. Managers must set daily rosters for their teams, using past performance to help make decisions. But which prior performance periods tell us the most about upcoming performance? To answer this, it's helpful to view a player’s future performance, for any given statistic, as a function of his performance in previous playing periods (e.g. previous game, previous week, previous year, etc.). In this on-going research project, we consider two approaches to predicting future performance from the past. In the first, we build a probability mass function for each of a set of discrete, disjoint past time periods and we use Expectation Maximization to learn the appropriate weights for each period to best predict future outcomes. In our second approach, we predict a player's performance in the next game based on all previous history using a recurrent neural network.
Document Type
Event
Start Date
14-5-2015 10:00 AM
End Date
14-5-2015 2:00 PM
Department
Computer Science
Genre/Form
student projects; posters
Subjects – Topical (LCSH)
Baseball--Computer network resources; Forecasting--Computer programs; Baseball--Statistical methods
Type
Image
Keywords
Baseball, Expectation maximization, Recurrent neural networks, Modeling
Rights
Copying of this document in whole or in part is allowable only for scholarly purposes. It is understood, however, that any copying or publication of this documentation for commercial purposes, or for financial gain, shall not be allowed without the author's written permission.
Language
English
Format
application/pdf
Included in
Time Series Modeling of Baseball Performance
The 162 game long Major League Baseball season provides ample time for a player’s performance to vary and trend in different directions. Managers must set daily rosters for their teams, using past performance to help make decisions. But which prior performance periods tell us the most about upcoming performance? To answer this, it's helpful to view a player’s future performance, for any given statistic, as a function of his performance in previous playing periods (e.g. previous game, previous week, previous year, etc.). In this on-going research project, we consider two approaches to predicting future performance from the past. In the first, we build a probability mass function for each of a set of discrete, disjoint past time periods and we use Expectation Maximization to learn the appropriate weights for each period to best predict future outcomes. In our second approach, we predict a player's performance in the next game based on all previous history using a recurrent neural network.