Sports Analytics: Using data to model athletic performance.
Master Python-based sports analytics, from data analysis to regression models. Learn to analyze team performance across major leagues.
Master Python-based sports analytics, from data analysis to regression models. Learn to analyze team performance across major leagues.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Sports Performance Analytics Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
4.4
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What you'll learn
Use Python to analyze and visualize sports performance data
Apply regression analysis to evaluate team and player statistics
Develop predictive models for sports outcomes
Create data representations and visualizations for different sports
Analyze salary impacts on team performance
Implement statistical methods to test sports phenomena like the hot hand effect
Skills you'll gain
This course includes:
6.2 Hours PreRecorded video
13 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 6 modules in this course
This comprehensive course introduces learners to sports analytics using Python, focusing on analyzing team performance across major sports leagues. The curriculum covers data representation techniques, regression analysis, and performance metrics, using real-world examples from MLB, NBA, NHL, EPL, and IPL. Students learn to extract meaningful insights from sports data, create analytical models, and develop practical skills in statistical analysis and data visualization, transforming them from consumers to producers of sports analytics.
Introduction to Sports Performance and Data
Module 1 · 9 Hours to complete
Introduction to Data Sources
Module 2 · 8 Hours to complete
Introduction to Sports Data and Plots in Python
Module 3 · 7 Hours to complete
Introduction to Sports Data and Regression Using Python
Module 4 · 7 Hours to complete
More on Regressions
Module 5 · 7 Hours to complete
Is There a Hot Hand in Basketball?
Module 6 · 8 Hours to complete
Fee Structure
Instructors
Stephen J. Galetti Professor of Sport Management
Stefan Szymanski is a renowned economist whose research is primarily focused on the economics of sports. With over 100 papers published in peer-reviewed journals and ten books authored, his work has had a significant impact on the field. Among his most well-known books is Soccernomics, co-authored with Simon Kuper, which became an international bestseller. Szymanski was born in Nigeria to a Polish father and an English mother. He spent the majority of his life in London before relocating to Michigan in 2011. As a professor at the University of Michigan, Szymanski continues to explore various aspects of sports economics, including the financial dynamics, market behaviors, and broader societal impacts of sports.
Assistant Professor in Sport Management
Wenche Wang is an Assistant Professor in Sport Management at the University of Michigan. Her research interests broadly focus on the economics of sports. She has published several articles exploring the internet market and antitrust issues in sports. Originally from China, Dr. Wang completed her Ph.D. in Economics at the University of Florida, where she also served as a postdoctoral associate in the Informatics Institute and the Department of Economics before joining the University of Michigan.
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4.4 course rating
177 ratings
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