Journal of Undergraduate Research
Keywords
Bayesian semi-parametric modeling, Major League Baseball, MLB
College
Physical and Mathematical Sciences
Department
Statistics
Abstract
Most measurements follow trends over time, and those trends can be modeled. While there are many techniques for doing this, this project’s model brings a unique angle. This method can model trends with multiple peaks, from different subjects, and group them in clusters of similar curves. This permits inference on behalf of the scientist as to what is similar between subjects within a group. We have applied this algorithm to data from Major League Baseball, due to its rich nature. We hope to draw a conclusion as to who the greatest batter of all-time is, discover which players might be undiscovered steroid users, and see what events in baseball history had lasting effects on the game.
Recommended Citation
Fisher, Jared and Fellingham, Dr. Gilbert
(2013)
"Bayesian Semi-parametric Modeling of Functional Data Exploration into Major League Baseball Analytics,"
Journal of Undergraduate Research: Vol. 2013:
Iss.
1, Article 2819.
Available at:
https://scholarsarchive.byu.edu/jur/vol2013/iss1/2819