About this Course
This course provides an introduction to using Python to analyze team performance in sports. Learners will discover a variety of techniques that can be used to represent sports data and how to extract narratives based on these analytical techniques. The main focus of the introduction will be on the use of regression analysis to analyze team and player performance data, using examples drawn from the National Football League (NFL), the National Basketball Association (NBA), the National Hockey League (NHL), the English Premier LEague (EPL, soccer) and the Indian Premier League (IPL, cricket). This course does not simply explain methods and techniques, it enables the learner to apply them to sports datasets of interest so that they can generate their own results, rather than relying on the data processing performed by others. As a consequence the learning will be empowered to explore their own ideas about sports team performance, test them out using the data, and so become a producer of sports analytics rather than a consumer. While the course materials have been developed using Python, code has also been produced to derive all of the results in R, for those who prefer that environment
What You'll Learn
- Use Python to analyze team performance in sports
- Represent sports data using various techniques
- Apply regression analysis to evaluate player and team performance
- Extract meaningful narratives from sports data
- Apply analytical methods on real sports datasets
Prerequisites
- Basic familiarity with Python programming
- Completion of introductory Python course recommended
Instructors
Wenche Wang
Former Assistant Professor in Sport Management
Stefan Szymanski
Stephen J. Galetti Professor of Sport Management