Authors:
Pedro Alceo
and
Roberto Henriques
Affiliation:
NOVA Information Management School, Campus de Campolide and Lisboa
Keyword(s):
Machine Learning, Data Mining, Predictive Analysis, Classification Model, Baseball, MLB.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Business Analytics
;
Computational Intelligence
;
Data Analytics
;
Data Engineering
;
Evolutionary Computing
;
Information Extraction
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Soft Computing
;
Symbolic Systems
Abstract:
As the world of sports expands to never seen levels, so does the necessity for tools which provided material advantages for organizations and other stakeholders. The main objective of this paper is to build a predictive model capable of predicting what are the odds of a baseball player getting a base hit on a given day, with the intention of both winning the game Beat the Streak and to provide valuable information for the coaching staff. Using baseball statistics, weather forecasts and ballpark characteristics several models were built with the CRISP-DM architecture. The main constraints considered when building the models were balancing, outliers, dimensionality reduction, variable selection and the type of algorithm – Logistic Regression, Multi-layer Perceptron, Random Forest and Stochastic Gradient Descent. The results obtained were positive, in which the best model was a Multi-layer Perceptron with an 85% correct pick ratio.