Machine Learning: Building a Logistic Regression Model to Predict the Outcome of an NBA Game

Machine Learning: Building a Logistic Regression Model to Predict the Outcome of an NBA Game

Prince K. Afriyie, PhD

The discipline of Data Science addresses the fundamental challenge of drawing robust conclusions about the world around us using incomplete data.This makes data science unique and applicable in almost every field of work or study. There are many facets of data science. One of the core features of data science is machine learning; this lecture will primarily focus on a topic of machine learning, namely regression. More specifically, we will use data from the 2017-18 season of the National Basketball Association (NBA) to build a logistic regression model to predict the outcome — probability of a win — of NBA home games.

Prince Afriyie is an assistant professor at the University of Virginia’s Department of Statistics. He is also affiliated with University of Texas’ Dana Center, where he helps develop career training courses for educators on teaching statistics. Dr. Afriyie received his PhD in Statistics at Temple University (2016), master’s degree in Mathematics at Ball State University (2011) and bachelor’s degree in Mathematics at Northern Kentucky University (2008). Prior to joining the University of Virginia, he was an assistant professor of Statistics at California Polytechnic State University, San Luis Obispo. Dr. Afriyie’s current research is focused on developing new and powerful methodologies for multiple comparisons, as well as statistics and data science education. He is a member of the Advanced Placement (AP) Statistics Development Committee where he helps write and review AP statistics exams questions and develop the course curriculum for AP Statistics.