Date of Project

4-16-2025

Document Type

Honors Thesis

School Name

College of Arts and Sciences

Department

Mathematics

Major Advisor

Dr. Michael Ackerman

Abstract

This paper analyzes the quantitative data of horses that ran in the Kentucky Derby to recognize statistically significant variables to predict the horse that comes in first or in-the-money. This analysis is specific to the post-implementation of the points system that began for the 2013 Kentucky Derby. Churchill Downs, the host of the Kentucky Derby, changed the methodology of qualification for a horse to enter the race; instead of qualifying with highest earnings in lifetime starts, the institution implemented a points system that awarded different proportions of points depending on the value of various prep races leading up to the Derby. This points system is known as the ‘Road to the Kentucky Derby’, commonly referred to as the Road. Incorporating evidence from conducted interviews, published articles and racing documentation, this analysis aims to show how the Road to the Kentucky Derby changed the way that horseplayers approach the race and identify specific variables that are statistically significant indicators of a successful horse. Using R Studio, this paper conducts a logistic regression of twenty-five variables such as, but not limited to, performance in a prep race, trainer performance and jockey performance. The analysis produced eight significant variables among two binary regression models.

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