Introduction to Sports Analytics

Introduction to Sports Analytics

using R

Edition 1.0 | Copyright 2025 | Publication Date: July 2024

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


Introduction to Sports Analytics using R prepares students for the programming and analytical challenges of Sports Analytics.  Key techniques covered using R and the Tidyverse 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 R) 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. 


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.



"I appreciate the practical approach of  "Sports Analytics Using R," which effectively teaches students through hands-on examples. It fills a gap in the market by providing a sports analytics text that utilizes R in a way that is accessible for undergraduate students with limited prior experience with the software."
Alex Traugutt, Elon University

“Very intuitive organization. … I love the balance of code and contextual understanding”
Bret Myers, Villanova University

"'Sports Analytics Using R' by Elmore & Urbaczewski fills a definite need in the marketplace. "
Ross M. Gosky, Appalachian State University

Table of Contents

Detailed Table of Contents PDF

Chapter 1: Introduction

Chapter 2: Introduction to R

Chapter 3: Web Scraping

Chapter 4: Baseball

Chapter 5: Basketball

Chapter 6: Football

Chapter 7: Hockey

Chapter 8: Soccer

Chapter 9: Golf

Chapter 10: Sport Wagering and Daily Fantasy Sports

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. He earned his Ph.D. in Statistics in 2003 from The Pennsylvania State University. 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.

Ryan Elmore

Andrew Urbaczewski is an Associate Professor at the University of Denver in the Department of Business Information & Analytics. He received his Ph.D. from Indiana University's Kelley School of Business in Management Information Systems in 1999. From 2013-2019, Andrew served as the first permanent chair of the Business Information & Analytics Department. In 2019–2021 Andrew was away as a distinguished visiting professor at the United States Air Force Academy. His 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.

Andrew Urbaczewski

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