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Sports Performance Analytics

Description

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.

Instructors

  • Christopher Brooks

    Associate Professor of Information

    School of Information

  • Wenche Wang

    Former Assistant Professor in Sport Management

    School of Kinesiology

  • Stefan Szymanski

    Stephen J. Galetti Professor of Sport Management

    School of Kinesiology

  • Youngho Park

    Former Lecturer of Sport Management

    School of Kinesiology

  • Peter F. Bodary

    Clinical Assistant Professor of Applied Exercise Science and Movement Science

    School of Kinesiology

Courses (5)