Generative AI
Sports analytics has become a field of study with growing popularity, fueled in part by real-world success illustrated by the best-selling book and movie Moneyball. The analysis of team and player performance data continues to revolutionize the sports industry, both on the field, court, and ice, and in the living rooms of fantasy sports and online sports bettors.
Using real datasets from 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 will learn how to build predictive models to forecast team and player performance. You will also replicate the success of Moneyball using real statistical models, apply a linear probability model (LPM) to predict categorical outcome variables in sports, explore how teams collect and organize athlete performance data using wearable technology, and apply machine learning in the context of sports analytics.
This introduction to sports analytics is designed for sports managers, coaches, physical therapists, and sports enthusiasts who want to understand the science behind sports performance and game prediction. The courses in this series will appeal to beginner Python programmers and data analysts who are looking for a fun and practical way to apply their Python, statistics, or predictive modeling skills.
Applied learning project
Students will apply the methods and techniques they have learned to sports data sets to produce their own results, rather than relying on the data processing performed by others. As a result, students will have the opportunity to explore their own ideas about the performance of sports teams, test them against the data, and thus become producers of sports analytics rather than consumers.
