Authors:
Michael Hamilton
1
;
Phuong Hoang
2
;
Lori Layne
3
;
Joseph Murray
2
;
David Padget
3
;
Corey Stafford
4
and
Hien Tran
2
Affiliations:
1
Rutgers University, United States
;
2
North Carolina State University, United States
;
3
MIT Lincoln Laboratory, United States
;
4
Columbia University, United States
Keyword(s):
Pitch Prediction, Feature Selection, ROC, Hypothesis Testing, Machine Learning.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Classification
;
Economics, Business and Forecasting Applications
;
Feature Selection and Extraction
;
Pattern Recognition
;
Theory and Methods
Abstract:
Major League Baseball, a professional baseball league in the US and Canada, is one of the most popular sports leagues in North America. Partially because of its popularity and the wide availability of data from games, baseball has become the subject of significant statistical and mathematical analysis. Pitch analysis is especially useful for helping a team better understand the pitch behavior it may face during a game, allowing the team to develop a corresponding batting strategy to combat the predicted pitch behavior. We apply several common
machine learning classification methods to PITCH f/x data to classify pitches by type. We then extend the classification task to prediction by utilizing features only known before a pitch is thrown. By performing significant feature analysis and introducing a novel approach for feature selection, moderate improvement over former results is achieved.