Election into the Naismith Memorial Basketball Hall of Fame is the crowning achievement of any basketball player's career. This exclusive group, which includes players, coaches, referees, teams, and other contributors to the game, contains just over 300 inductees. We construct several predictive models that classify a given player as either being elected into the Hall of Fame, or not, based on their career statistical output and achievements. The goal here is to make future predictions about who might be inducted into the Hall of Fame. As a basis for this research we provide an understanding of (binary) logistic regression theory. This regression method also leads us to areas in mathematics such as maximum likelihood estimation and a multivariable Newton-Raphson iteration method. This independent study project touches on the histories of the game of basketball, what it takes to become enshrined into the Naismith Memorial Basketball Hall of Fame, and of logistic regression's origin. Furthermore, we use maximum likelihood theory, along with the Newton-Raphson iteration method, to help us understand how logistic regression works. Finally, we experiment with a few different models, explore diagnostics for those models, and elaborate on their implications.
Handloser, Robert P., "Hall of Fame Classification Using Logistic Regression Analysis" (2016). Senior Independent Study Theses. Paper 7345.
Analysis | Applied Mathematics
Bachelor of Arts
Senior Independent Study Thesis
© Copyright 2016 Robert P. Handloser