Abstract:

This is a special two-part sports analytics workshop series. Part one is planned for Fall 2026 and part two is planned for Spring 2027.

Sports Analytics 1: Fundamentals
This course provides an introduction to the tools, data, and statistical concepts used in modern sports analytics. Participants will learn how to access and work with publicly available sports data, including play-by-play, player statistics, and player-tracking data from sources such as nflscrapR and baseballr, as well as data from the NFL Big Data Bowl. The course emphasizes hands-on analysis and visualization using R packages including ggplot2, nflplotR, sportyR, teamcolors, and gganimate. Participants will then use these data to explore fundamental concepts in sports analytics, including expected points and win probability. By the end of the course, participants will have the skills to obtain, visualize, and begin analyzing real-world sports data while developing an understanding of how statistical models can be used to quantify performance and decision-making in sports.

Bio:

Gregory J. Matthews, Ph.D., is a Professor of Statistics in the Department of Mathematics and Statistics at Loyola University Chicago and Director of the Center for Data Science and Consulting. His research focuses on sports analytics, missing data methods, statistical shape analysis, and statistical privacy. He earned his Ph.D. in Statistics from the University of Connecticut in 2011.

Matthews received the 2025 Significant Contributor Award from the American Statistical Association’s Section on Statistics in Sports. He was a finalist in the 2023 NFL Big Data Bowl with Quang Nguyen and has been a four-time finalist and two-time winner (2020 and 2021) of the Carnegie Mellon Sports Analytics Conference Reproducible Research Competition. He also recently helped establish the Journal of Statistics and Data Science in Sports, a diamond open-access journal.

He lives in Chicago with his wife and three children.