Elmore: Sports Analytics using Python

Introduction to Sports Analytics

using Python

Edition 1.0 | Copyright | Publication Date: October 2024

Ryan Elmore, University of Denver
Andrew Urbaczewski, University of Denver

Overview

Introduction to Sports Analytics using Python prepares students for the programming and analytical challenges of Sports Analytics. Key techniques covered include data acquisition/web scraping, regression, prediction, simulation, visualization, and cluster models. Sports covered include baseball, basketball, football, ice hockey, soccer, golf, sports betting, and daily fantasy sports. However, the techniques and insights learned should prove valuable for studying other sports. Students are expected to have had a programming course (though not necessarily Python) and a statistics class. A prior course in linear programming and optimization models will provide advanced insight into the decision-making processes covered but is not necessary.

Features

Provide hands-on experiences
Students are taught the fundamental methodology first. This serves as the foundation to subsequent analyses and data manipulations. In this manner, students learn much more than how to talk about sports. They learn to generate, manipulate, visualize, and analyze illuminating data.

Foster the use of visualizations for analysis and presentation
The use and importance of data visualizations are stressed throughout both as a way of more richly understanding problems and as a way to present key findings, especially to non-technical audiences.

Motivate students with real-world problems
Each chapter about a sport includes a case study regarding a key challenge that invites analysis. These are designed to motivate learning and stimulate discussion.

Prepare students for further advanced work
Each chapter includes a Research Block that engages sports analytic questions currently addressed in the literature and provides links. In an evolving, highly competitive field, this provides students pursuing a career in sports analytics with an exceedingly valuable skill, habit, and resource.

About the Authors

Ryan Elmore is an Associate Professor in the Department of Business Information and Analytics, at University of Denver's Daniels College of Business. His work has been featured in The Economist, The Wall Street Journal, The Telegraph, The Guardian, and The Fried Egg Podcast. His academic work in sports statistics has led to the position of Associate Editor for the Journal of Quantitative Analysis of Sports (2015–present).

In addition to his academic responsibilities, Dr. Elmore is a consultant to the Denver Nuggets professional basketball team. In 2022, he co-founded First Team Analytics; a private company that develops dashboards for sports teams that places the actionable information they need front-and-center. He earned his Ph.D. in Statistics in 2003 from The Pennsylvania State University.

Andrew Urbaczewski is an Associate Professor at the University of Denver in the Department of Business Information & Analytics. From 2013-2019, he served as the first permanent chair of the Business Information & Analytics Department. In 2019–2021, he was away as a distinguished visiting professor at the United States Air Force Academy.

Dr. Urbaczewski's research explores sports analytics, as well as information security education, and electronic health record implementation. He has served as editor-in-chief of The Journal of Information Technology Cases and Applied Research. His hobbies include golf, skiing, distance running, and aviation. He received his Ph.D. from Indiana University's Kelley School of Business in Management Information Systems in 1999.