Prediction Models with Sports Data
Master predictive modeling in sports using Python to forecast game outcomes across major leagues like EPL, NBA, and NHL.
Master predictive modeling in sports using Python to forecast game outcomes across major leagues like EPL, NBA, and NHL.
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.
4.5
(36 ratings)
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What you'll learn
Generate accurate forecasts for professional sports games
Build logistic regression models for game predictions
Evaluate betting market efficiency using statistical methods
Apply predictive modeling across different sports leagues
Understand the relationship between analytics and sports betting
Skills you'll gain
This course includes:
6.9 Hours PreRecorded video
5 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 5 modules in this course
This comprehensive course teaches students how to build predictive models for sports outcomes using Python. The curriculum covers logistic regression modeling, betting market analysis, and game result forecasting across major sports leagues. Students learn to evaluate model reliability using betting odds data, analyze market efficiency, and understand the relationship between analytics and sports betting. The course also addresses historical and social aspects of sports gambling, including ethical considerations and risk management.
Week 1
Module 1 · 9 Hours to complete
Week 2
Module 2 · 7 Hours to complete
Week 3
Module 3 · 8 Hours to complete
Week 4
Module 4 · 6 Hours to complete
Week 5
Module 5 · 1 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.
Lecturer of Sport Management
Youngho Park is a Lecturer in Sport Management at the University of Michigan. His research focuses on Sport Fan Analytics, Consumer Behavior, and Sports Analytics, areas crucial for understanding fan engagement and decision-making in the sports industry. His academic journey includes obtaining a Ph.D. in Sport Management from Ohio State University in 2015, and he has been teaching at Michigan since 2016. His expertise lies in using data to explore trends and behaviors in sports, which is crucial for developing strategies to engage fans and optimize business models in sports organizations.Park’s course, Prediction Models with Sports Data, likely covers the application of data science and statistical techniques to predict trends and outcomes in sports. This could include using machine learning algorithms and statistical modeling to analyze fan behavior, team performance, and other variables in sports management.
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4.5 course rating
36 ratings
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