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

U-M Free Access Enrollment Options

What You'll Learn

Use Python to analyze team performance in sports.
Become a producer of sports analytics rather than a consumer.

6 Modules

48 Hours

8 hrs per module (approx.)

Rating

About 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 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.

Skills You'll Gain

What You'll Earn

Certificate of Completion:: Certificates of completion acknowledge knowledge acquired upon completion of a non-credit course or program.

Experience Type: 100% Online
Format: Self-Paced
Series: Sports Performance Analytics
Subject: Business

Data Science

Technology
Platform: Coursera

Welcome Message

Foundations of Sports Analytics: Data, Representation, and Models in Sports introduces learners to analyzing sports performance using data and regression techniques. Working with real datasets across multiple sports leagues, you will apply Python-based methods to explore performance narratives and test analytical ideas. The course, part of the Sports Performance Analytics series, emphasizes hands-on learning and independent analysis.

This abbreviated syllabus description was created with the help of AI tools and reviewed by staff. The full syllabus is available to those who enroll in the course.

Course Schedule

Module 1: Introduction to Sports Performance and Data

Reading: Course Syllabus
Reading: Help Us Learn More About You
Video: Introduction to Foundations and Instructor Stefan Szymanski
Video: Faculty Introduction: Wenche Wang
Video: Pythagorean Expectation & Baseball Part 1
Video: Pythagorean Expectation & Baseball Part 2
Video: Pythagorean Expectation & the IPL
Video: Pythagorean Expectation & the NBA
Video: Pythagorean Expectation & English Football
Video: Pythagorean Expectation as a Predictor in the MLB
Reading: A Note on Notebooks
Ungraded Lab: Pythagorean expectation and MLB
Ungraded Lab: Pythagorean expectation and MLB - Self Test Solutions
Ungraded Lab: Pythagorean expectation and the IPL
Ungraded Lab: Pythagorean expectation and the NBA
Ungraded Lab: Pythagorean expectation and English Football
Ungraded Lab: Pythagorean expectation as a Predictor in MLB
Reading: Assignment Overview
Ungraded Lab: Assignment 1 Workspace
Reading: Week 1 - Sample Notebook
Reading: Week 1 R Content

Module 2: Introduction to Data Sources

Video: Accessing Data in Python I
Video: Accessing Data in Python II
Video: Data Exploration
Video: Summary Statistics
Video: More on Summary Statistics
Video: Correlation Analysis
Ungraded Lab: Accessing Data Using Python
Ungraded Lab: Data Exploration and Summary Statistics
Ungraded Lab: Summary Statistics and Correlation Analysis
Ungraded Lab: Week 2 - Self Test Solutions
Reading: Assignment Overview
Ungraded Lab: Assignment 2 Workspace
Reading: Assignment Instructions- Part 1
Reading: Assignment Instructions- Part 2
Reading: Assignment Instructions- Part 3
Reading: Week 2 - Sample Notebook
Reading: Week 2 R Content

Module 3: Introduction to Sports Data and Plots in Python

Video: Data Representation: Cricket Pt. 1
Video: Data Representation: Cricket Pt. 2
Video: Data Representation: Baseball
Video: Data Representation: Basketball
Ungraded Lab: Basketball Heatmap
Ungraded Lab: Indian Premier League Graphs
Ungraded Lab: Simple Heatmaps Baseball
Reading: Assignment Overview
Ungraded Lab: Week 3 Assignment - Part 1 - Workspace
Reading: Assignment Instructions - Part 1
Reading: Week 3 - Part 1 - Sample Notebooks
Ungraded Lab: Week 3 Assignment - Part 2 - Workspace
Reading: Assignment Instructions - Part 2
Reading: Week 3 - Part 2 - Sample Notebook
Reading: Week 3 R Content

Module 4: Introduction to Sports Data and Regression Using Python

Video: Introduction to Regression Analysis
Video: Interpreting Regression Results
Video: More on Regressions
Video: Regression Analysis - Intro to Cricket Data
Video: Regression Analysis - Batsman's performance and salary
Video: Regression Analysis - Bowler's performance and salary
Ungraded Lab: Introduction to Regression Analysis
Ungraded Lab: Introduction to Regression Analysis - Self Test Solutions
Ungraded Lab: Regression Analysis with Cricket Data
Reading: Assignment Overview
Ungraded Lab: Week 4 - Assignment Workspace
Reading: Assignment Instructions - Part 1
Reading: Assignment Instructions- Part 2
Reading: Assignment Instructions- Part 3
Reading: Week 4 - Sample Notebook
Reading: Week 4 R Content

Module 5: More on Regressions

Video: Using regression analysis - an example with NBA data
Video: Using regression analysis - an example with EPL data
Video: Using regression analysis - an example with MLB data
Video: Using regression analysis - an example with NHL data
Ungraded Lab: EPL
Ungraded Lab: Hockey
Ungraded Lab: MLB
Ungraded Lab: NBA
Reading: Assignment Overview
Ungraded Lab: Week 5 - Assignment Workspace
Reading: Assignment Instructions
Reading: Week 5 - Sample Notebook
Reading: Week 5 R Content

Module 6: Is There a Hot Hand in Basketball?

Video: Hot Hand: Phenomenon or Fallacy?
Video: NBA Shot Log Data Preparation I
Video: NBA Shot Log Data Preparation II
Video: Conditional Probability
Video: Conditional and Unconditional Probabilities
Video: Autocorrelation
Video: Regression Analysis on Hot Hand I
Video: Regression Analysis on Hot Hand II
Ungraded Lab: Understanding and Cleaning the NBA Shot Log Data
Ungraded Lab: Using Summary Statistics to Examine the Hot Hand
Ungraded Lab: Using Regression Analysis to Test the Hot Hand
Ungraded Lab: Using Regression Analysis to Test the Hot Hand - Self Test Solutions
Reading: Assignment Overview
Ungraded Lab: Week 6 - Assignment Workspace
Reading: Assignment Instructions - Part 1
Reading: Assignment Instructions - Part 2
Reading: Assignment Instructions - Part 3
Reading: Week 6 - Sample Notebook
Reading: Post-Course Survey
Reading: Week 6 R Content

Grading Policy

Final grades are based entirely on quizzes administered throughout the six weeks. Quiz weights range from approximately 5.5% to 16.7%, collectively totaling 100%.

Stefan Szymanski

Stephen J. Galetti Professor of Sport Management

Wenche Wang

Former Assistant Professor in Sport Management

Intermediate Level

Learners should have some familiarity with Python before starting this course. We recommend the Python for Everybody Specialization.

Individuals

This experience is available to individual learners on the following platforms:

U-M Community

Students, faculty, staff, and alumni of the University of Michigan get free access.

Organizations

Special pricing and tailored programming bundles available for organizational partners.

4.4

167 Ratings from Coursera

Most Recent Reviews

Read all reviews

July 12, 2025

Thorough and with lots of practice to help retain information.

July 11, 2025

The course is moderately interesting from Module 1 to Module 5, but it is not entirely clear who the target audience of the course may be. The course presumes some existing knowledge of Python programming, statistical methods, and the sports included in the course. However, it is not entirely clear to me what learners already equipped with this knowledge are supposed to benefit from this course. In the course, it is demonstrated how these statistical methods can be applied to some selected sports in Python (R is also supported to some moderate extent), but learners who already know Python, statistical methods, and the sports will probably already be able to do this by themselves. If the course is supposed to provide that little extra push that is required to put these all together, it's fine, but you will not actually learn any of these categories, only how to apply them in a very specific context. OK, so then we reach Module 6, where all this collapses in a thunderous rumble. The amount of omissions, glitches, oversights, discrepancies, and pure nonsense in Module 6 is just way too much to even start listing and makes one think what is put out to customers as the final product is in fact some sort of vague first draft of the module, waiting to be further developed and reviewed by an army of content developers. Very disheartening, and one doesn't quite understand why Coursera or the UoM would voluntarily put out such rubbish only to embarrass themselves, when they strictly speaking do not really have to do so. Really, why? It's a mystery! For the moderately engaging and useful content of the first five modules, I am giving this course 2 stars, but the amount of damage Module 6 has visited upon the perceived quality of this course and the reputation of Coursera and the UoM is so devastating that any further benevolence is just simply out of the question. Ah yes, and it would have been great to have a lecturer in statistics who can pronounce the word "statistics" once I am charged for the lectures! Thank you for reading my review.

October 14, 2024

Excellent course! All of a sudden, I understand statistical concepts I struggled to grasp in undergrad.

December 15, 2023

cant find data needed for course

September 06, 2023

Really great and informative course, loved the material and the assignments!

June 12, 2023

IN GENERAL TERMS I LIKE IT ALL, WITH THE EXCEPT THAT I COULD NOT FINISH THE SPECIALIZED PROGRAM BECAUSE I DID NOT UNDERSTAND THE QUESTIONS OF COURSE NUMBER 5, THE TEACHER ASKS THINGS THAT HE DOESN'T EXPLAIN, AND WHAT IT EXPLAINES DOES NOT DO IT WITH CLARITY !!! I AM NOT AN EXPERT IN PYTHON, BUT LITTLE BY LITTLE I WAS LEARNING SOMETHING NEW, BUT COURSE NUMBER 5 SEEMED IMPOSSIBLE. I AM AN EXPERT IN ANALYZING SPORTS STATISTICS, AND I TAKEN THE SPECIALIZED PROGRAM BECAUSE I WANTED TO LEARN NEW THINGS THAT WILL HELP ME IN MY JOB; AND IN COURSE 4 I LEARNED MANY NEW AND VERY INTERESTING THINGS; BUT I COULDN'T FINISH THE SPECIALIZATION BECAUSE COURSE NUMBER 5 IS ANTI-PEDAGOGICAL IF YOU ARE NOT AN EXPERT IN PYTHON I DO NOT RECOMMEND THIS COURSE !!!

February 17, 2023

The course content was intriguing. However, it definitely needs updating. There are times where assignment instructions are incomplete, or there are discrepancies between what is shown in the lecture and what is in the notebooks. There are also times where what is in the assignment quizzes doesn't match up with the actual data in the assignment notebooks. I didn't get a lot out of some of the weeks, as it felt like the instructor was just reading word for word what was written in the notebooks, not adding any additional commentary or explanation. Furthermore, I wish they would have dove deeper into some of the statistics and math behind some of the concepts they showed. They would introduce a concept a lot of times, but not explain what it meant or how it related to other concepts we had learned.

August 28, 2022

Excellent course on how data analytics can be used in the world of sports.

January 16, 2022

Some lectures seem to be unnecessary as they are repetitions of same concepts on different datasets.

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

What You'll Learn