This course is part of the Sports Performance Analytics Specialization

4.4
stars
129 ratings

Wenche Wang +1 more instructor

13,020 already enrolled

Offered By

Sports Performance Analytics SpecializationUniversity of Michigan

About this Course

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This course provides an introduction to using Python to analyze team performance in sports. Learners will discover a variety of techniques that can be used to represent sports data and how to extract narratives based on these analytical techniques. The main focus of the introduction will be on the use of regression analysis to analyze team and player performance data, using examples drawn from the National Football League (NFL), the National Basketball Association (NBA), the National Hockey League (NHL), the English Premier LEague (EPL, soccer) and the Indian Premier League (IPL, cricket). 
This course does not simply explain methods and techniques, it enables the learner to apply them to sports datasets of interest so that they can generate their own results, rather than relying on the data processing performed by others.  As a consequence the learning will be empowered to explore their own ideas about sports team performance, test them out using the data, and so become a producer of sports analytics rather than a consumer.

While the course materials have been developed using Python, code has also been produced to derive all of the results in R, for those who prefer that environment.

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Shareable Certificate

Earn a Certificate upon completion

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Coursera Labs

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Course 1 of 5 in the

Sports Performance Analytics Specialization

Approx. 49 hours to complete

English

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Course 1 of 5 in the

Sports Performance Analytics Specialization

Approx. 49 hours to complete

English

Instructors

Instructor rating

4.37/5 (38 Ratings)

Wenche Wang

Assistant Professor in Sport Management

School of Kinesiology

13,020 Learners

1 Course

Stefan Szymanski

Stephen J. Galetti Professor of Sport Management

School of Kinesiology

14,387 Learners

3 Courses

Offered by

University of Michigan

Syllabus - What you will learn from this course

Week1

Week 1

10 hours to complete

Introduction to Sports Performance and Data

This week introduces a simple example of sports analytics in practice - the calculation of the Pythagorean expectation to model winning in team sports. This can also be used for the purposes of prediction. Examples are developed for five different sports leagues, 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).

10 hours to complete

8 videos (Total 78 min), 6 readings, 1 quiz

8 videos

Introduction to Foundations and Instructor Stefan Szymanski6mFaculty Introduction: Wenche Wang35sPythagorean Expectation & Baseball Part 1 18mPythagorean Expectation & Baseball Part 212mPythagorean Expectation & the IPL12mPythagorean Expectation & the NBA5mPythagorean Expectation & English Football9mPythagorean Expectation as a Predictor in the MLB13m

6 readings

Course Syllabus10mHelp Us Learn More About You5mA Note on Notebooks10mAssignment Overview10mWeek 1 - Sample Notebook10mWeek 1 R Content10m

1 practice exercise

Week 1 Quiz30m

Week2

Week 2

9 hours to complete

Introduction to Data Sources

This week will use NBA data to introduce basic and important Python codes to conduct data cleaning and data preparation. This week also discusses summary and descriptive analyses with statistics and graphs to understand the distribution of data, the characteristics and pattern of variables as well as the relationship between two variables. At the end of this week, we will introduce correlation coefficients to summarize the linear relationship between two variables.

9 hours to complete

6 videos (Total 67 min), 6 readings, 3 quizzes

6 videos

Accessing Data in Python I13mAccessing Data in Python II11mData Exploration10mSummary Statistics7mMore on Summary Statistics11mCorrelation Analysis12m

6 readings

Assignment Overview10mAssignment Instructions- Part 110mAssignment Instructions- Part 210mAssignment Instructions- Part 310mWeek 2 - Sample Notebook10mWeek 2 R Content10m

3 practice exercises

Week 2 - Quiz 130mWeek 2 - Quiz 230mWeek 2 - Quiz 330m

Week3

Week 3

8 hours to complete

Introduction to Sports Data and Plots in Python

8 hours to complete

4 videos (Total 52 min), 6 readings, 2 quizzes

Week4

Week 4

7 hours to complete

Introduction to Sports Data and Regression Using Python

This week introduces the fundamentals of regression analysis. We will discuss how to perform regression analysis using Python and how to interpret regression output. We will use NHL data to estimate multiple regression models to identify the team level performance factors that affect the team's winning percentage. We will also use cricket data from the Indian Premier League to run regression analyses to examine whether player performance impacts player salary.

7 hours to complete

6 videos (Total 55 min), 6 readings, 3 quizzes

6 videos

Introduction to Regression Analysis 10mInterpreting Regression Results8mMore on Regressions8mRegression Analysis - Intro to Cricket Data11mRegression Analysis - Batsman's performance and salary8mRegression Analysis - Bowler's performance and salary8m

6 readings

Assignment Overview10mAssignment Instructions - Part 110mAssignment Instructions- Part 210mAssignment Instructions- Part 310mWeek 4 - Sample Notebook10mWeek 4 R Content10m

3 practice exercises

Week 4 - Quiz 130mWeek 4 - Quiz 230mWeek 4 - Quiz 330m

Reviews

4.4

25 reviews

5 stars
63.07%
4 stars
24.61%
3 stars
5.38%
2 stars
3.84%
1 star
3.07%

TOP REVIEWS FROM FOUNDATIONS OF SPORTS ANALYTICS: DATA, REPRESENTATION, AND MODELS IN SPORTS

by HHSep 4, 2021

Great material and well paced for people working. One instructor is a bit green though.

by KYAug 26, 2021

Great course. Although this course focuses on sports analysis, the analyzing process I learned from it can apply to any other areas of analysis.

by KJJul 5, 2021

Best course to interact with data representation programming and libraries, especially for the great sports fan.

by MHMar 7, 2022

An excellent way to get hands-on experience exploring sports data in Python/R

View all reviews

About the Sports Performance Analytics Specialization

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.

Frequently Asked Questions

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When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
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Foundations of Sports Analytics: Data, Representation, and Models in Sports

About this Course