We may earn an affiliate commission when you visit our partners.
Course image
Christopher Brooks, Stefan Szymanski, Wenche Wang, Youngho Park, and Peter F. Bodary

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.

Read more

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.

Enroll now

Share

Help others find Specialization from Coursera by sharing it with your friends and followers:

What's inside

Five courses

Foundations of Sports Analytics: Data, Representation, and Models in Sports

This course provides an introduction to using Python to analyze team performance in sports. Learners will discover techniques for representing sports data and extracting narratives based on analytical techniques. The main focus will be on regression analysis to analyze team and player performance data, using examples from the NFL, NBA, NHL, EPL, and IPL.

Moneyball and Beyond

The book Moneyball revolutionized the analysis of performance statistics in professional sports, showing that data analytics could be used to increase team winning percentage. This course shows how to program data using Python to test the claims that lie behind the Moneyball story, and to examine the evolution of Moneyball statistics since the book was published.

Prediction Models with Sports Data

In this course, learners will learn how to generate forecasts of game results in professional sports using Python. The focus is on logistic regression as a way of modeling game results, using data on team expenditures. Learners will model past results and use the model to forecast the outcome of games not yet played. The course will show learners how to evaluate the reliability of a model using data on betting odds. The analysis is applied to the English Premier League, the NBA, and the NHL. The course also provides an overview of the relationship between data analytics and gambling, its history, and the social issues that arise in relation to sports betting, including the personal risks.

Wearable Technologies and Sports Analytics

Sports analytics use data from athletes to quantify training and competition efforts. Wearable technology devices provide opportunities to study the stress and recovery of athletes. This course introduces wearable technology devices and their use in training and competition. It includes an introduction to the physiological principles relevant to exercise training and sport performance and how wearable devices can be used to characterize both training and performance.

Introduction to Machine Learning in Sports Analytics

In this course, students will explore supervised machine learning techniques using the Python scikit-learn (sklearn) toolkit and real-world athletic data to understand both machine learning algorithms and how to predict athletic outcomes. By the end of the course, students will have a broad understanding of how classification and regression techniques can be used to enable sports analytics across athletic activities and events.

Learning objectives

  • Understand how to construct predictive models to anticipate team and player performance.
  • Understand the science behind athlete performance and game prediction.
  • Engage in a practical way to apply their python, statistics, or predictive modeling skills.

Save this collection

Save Sports Performance Analytics to your list so you can find it easily later:
Save
Our mission

OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.

Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.

Find this site helpful? Tell a friend about us.

Affiliate disclosure

We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.

Your purchases help us maintain our catalog and keep our servers humming without ads.

Thank you for supporting OpenCourser.

© 2016 - 2024 OpenCourser