Abstract
In this paper, we will discuss a method of building a predictive model for Major League Baseball Games. We detail the reasoning for pursuing the proposed predictive model in terms of social popularity and the complexity of analyzing individual variables. We apply a coarse-grain outlook inspired by Simon Dedeos' work on Human Social Systems, in particular the open source website Wikipedia [2] by attempting to quantify the influence of winning and losing streaks instead of analyzing individual performance variables. We will discuss initial findings of data collected from the LA Dodgers and Colorado Rockies and apply further statistical analysis to find optimal betting points using a coarse-grain approach. We will apply Bayes' Theorem to add predictive power to a naive model using winning and losing streaks. We will discuss possible shortcomings of the proposed using Bayes' approach and address the question as to whether or not baseball wins and losses can be produced using a random process.
Advisor
Kim In-Jae
Committee Member
Deepak Sanjel
Committee Member
Han Wu
Date of Degree
2014
Language
english
Document Type
Thesis
Degree
Master of Science (MS)
Department
Mathematics and Statistics
College
Science, Engineering and Technology
Recommended Citation
Tait, J. R. (2014). Building a Predictive Model for Baseball Games [Master’s thesis, Minnesota State University, Mankato]. Cornerstone: A Collection of Scholarly and Creative Works for Minnesota State University, Mankato. https://cornerstone.lib.mnsu.edu/etds/382/
Creative Commons License
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Included in
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Rights Statement
