Associate Professor of Information
9 Learning Experiences
Sports Performance Analytics
What You'll Learn
- Understand how to construct predictive models to anticipate team and player performance
- Engage in a practical way to apply their Python, statistics, or predictive modeling skills
- Understand the science behind athlete performance and game prediction
5-Course Series
152 hours
31 hours per course (approx.)
Shareable Certificate
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About Sports Performance Analytics
Sports analytics has emerged as a field of research with increasing popularity propelled, in part, by the real-world success illustrated by the best-selling book and motion picture, Moneyball. Analysis of team and player performance data has continued to revolutionize the sports industry on the field, court, and ice as well as in living rooms among fantasy sports players and online sports gambling.
Drawing from real data sets in Major League Baseball (MLB), the National Basketball Association (NBA), the National Hockey League (NHL), the English Premier League (EPL-soccer), and the Indian Premier League (IPL-cricket), you’ll learn how to construct predictive models to anticipate team and player performance. You’ll also replicate the success of Moneyball using real statistical models, use the Linear Probability Model (LPM) to anticipate categorical outcomes variables in sports contests, explore how teams collect and organize an athlete’s performance data with wearable technologies, and how to apply machine learning in a sports analytics context.
This introduction to the field of sports analytics is designed for sports managers, coaches, physical therapists, as well as sports fans who want to understand the science behind athlete performance and game prediction. New Python programmers and data analysts who are looking for a fun and practical way to apply their Python, statistics, or predictive modeling skills will enjoy exploring courses in this series.
Skills You'll Gain
- Python (Programming Language)
- Sports Analytics
- Data Visualization
- Machine Learning
- Statistical Modeling
- Statistical Analysis
- Data Analysis
What You'll Earn
- Certificate of Completion
- Certificates of completion acknowledge knowledge acquired upon completion of a non-credit course or program.
Former Assistant Professor in Sport Management
1 Learning Experience
Stephen J. Galetti Professor of Sport Management
3 Learning Experiences
Former Lecturer of Sport Management
1 Learning Experience
Clinical Assistant Professor of Applied Exercise Science and Movement Science
1 Learning Experience
Course content developed by U-M faculty and managed by the university. Faculty titles and affiliations are updated periodically.
Intermediate Level
Learners should have some familiarity with Python before starting this course. We recommend the Python for Everybody Specialization.
Series Video
5 Courses in this series
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Free U-M Access CourseFoundations of Sports Analytics: Data, Representation, and Models in Sports
Use Python and sports datasets to explore team performance and become a hands-on producer of sports analytics.
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Free U-M Access CourseMoneyball and Beyond
Use Python to analyze baseball performance data and explore the evolution of Moneyball-era statistics through hands-on coding.
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Free U-M Access CoursePrediction Models with Sports Data
Use logistic regression and Python to model and predict sports outcomes while examining analytics in gambling and society.
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Free U-M Access CourseWearable Technologies and Sports Analytics
Analyze athletic performance and recovery using wearable tech, physiological principles, and Python programming on sports datasets.
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Free U-M Access CourseIntroduction to Machine Learning in Sports Analytics
Apply machine learning techniques to real sports data to analyze, predict outcomes, and enhance performance analytics.
