Sports Performance Analytics Specialization
Sports analytics is becoming more prominent as an area of study, thanks in part to the real-world success of the best-selling book and film Moneyball. On the field, court, and ice, as well as in living rooms among fantasy sports players and online sports gambling, data analysis of team and player performance has continued to change the sports industry.
You'll learn how to build predictive models to predict team and player performance using real data sets 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'll also learn how to use real statistical models to replicate Moneyball's success, how to use the Linear Probability Model (LPM) to predict categorical outcomes variables in sports contests, how to use wearable technologies to collect and organize an athlete's performance data, and how to apply machine learning in a sports analytics context.
This introduction to sports analytics is intended for sports management, coaches, physical therapists, and sports fans interested in learning more about the science underlying player performance and game prediction. This set of courses will appeal to new Python programmers and data analysts who are searching for a fun and useful method to put their Python, statistics, or predictive modeling skills to use.
Instead of depending on the data processing of others, learners will use the methodologies and techniques acquired to sports datasets to develop their own outcomes. As a result, the student will be able to explore their own ideas regarding sports team performance, test them using data, and thus become a creator rather than a consumer of sports statistics.
Instructors: Wenche Wang and 4 more instructors
Coursera rate: 4.5/5.0, 101 ratings
Offered by: University of Michigan
Enroll here: https://www.coursera.org/specializations/sports-analytics
