Moneyball and Beyond

U-M Free Access Enrollment Options

What You'll Learn

Program data using Python to test the claims that lie behind the Moneyball story.
Use statistics to conduct your own team and player analyses.

5 Modules

30 Hours

6 hrs per module (approx.)

Rating

About Moneyball and Beyond

The book Moneyball triggered a revolution in the analysis of performance statistics in professional sports, by 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. The learner is led through the process of calculating baseball performance statistics from publicly available datasets. The course progresses from the analysis of on base percentage and slugging percentage to more advanced measures derived using the run expectancy matrix, such as wins above replacement (WAR). By the end of this course the learner will be able to use these statistics to conduct their own team and player analyses.

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

Welcome to Moneyball and Beyond, a data-driven exploration of sports analytics using Python. Learners analyze baseball performance metrics, test claims from Moneyball, and build advanced statistics such as run expectancy and wins above replacement to evaluate teams and players.
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

Reading: Course Syllabus
Reading: Help Us Learn More About You
Video: Introduction to Moneyball
Video: Reproducing Table 1 of Hakes and Sauer - Part 1
Video: Reproducing Table 1 of Hakes and Sauer - Part 2
Video: Reproducing Table 1 of Hakes and Sauer- Part 3
Video: Reproducing Table 1 of Hakes and Sauer - Part 4
Ungraded Lab: Lecture - H&S Table
Reading: Week 1 - Assignment Overview
Ungraded Lab: Assignment 1 Workspace
Reading: Assignment - Part 1
Reading: Sample Notebook - Part 1
Reading: Assignment - Part 2
Reading: Sample Notebook - Part 2
Reading: Assignment - Part 3
Reading: Sample Notebook - Full Sample
Reading: Week 1 R Content
Graded: Week 1 - Quiz 1
Graded: Week 1 - Quiz 2
Graded: Week 1 - Quiz 3

Module 2

Video: Reproducing Table 3 of Hakes and Sauer- Part 1
Video: Reproducing Table 3 of Hakes and Sauer- Part 2
Video: Reproducing Table 3 of Hakes and Sauer- Part 3
Video: Reproducing Table 3 of Hakes and Sauer- Part 4
Video: Reproducing Table 3 of Hakes and Sauer- Part 5
Video: Reproducing Table 3 of Hakes and Sauer- Part 6
Ungraded Lab: Lecture - Moneyball Table 3
Reading: Moneyball Week 2 - Assignment Overview
Ungraded Lab: Assignment 2 Workspace
Reading: Assignment - Part 1
Reading: Sample Notebook - Part 1
Reading: Assignment - Part 2
Reading: Sample Notebook - Part 2
Reading: Assignment - Part 3
Reading: Sample Notebook - Full Sample
Reading: Week 2 R Content
Graded: Week 2 - Quiz 1
Graded: Week 2 - Quiz 2
Graded: Week 2 - Quiz 3

Module 3

Video: Moneyball update Part 1
Video: Moneyball update Part 2
Video: Moneyball Update Part 3
Video: Moneyball Update Part 4
Video: Moneyball Update Part 5
Video: Moneyball Update Part 6
Ungraded Lab: Lecture - Moneyball Update
Reading: Moneyball Week 3 - Assignment Overview
Ungraded Lab: Assignment 3 Workspace
Reading: Assignment - Part 1
Reading: Sample Notebook - Part 1
Reading: Assignment - Part 2
Reading: Sample Notebook - Part 2
Reading: Assignment - Part 3
Reading: Sample Notebook - Part 3
Reading: Sample Notebook in R
Reading: Week 3 R Content
Graded: Week 3 - Quiz 1
Graded: Week 3 - Quiz 2
Graded: Week 3 - Quiz 3

Module 4

Video: Beyond Moneyball: Run expectancy Part 1
Video: Beyond Moneyball: Run Expectancy Part 2
Video: Beyond Moneyball: Run expectancy Part 3
Video: Beyond Moneyball: Run expectancy Part 4
Ungraded Lab: Lecture - Run Expectancy
Reading: Moneyball Week 4 - Assignment Overview
Ungraded Lab: Assignment 4 Workspace
Reading: Assignment - Part 1
Reading: Sample Notebook - Part 1
Reading: Assignment - Part 2
Reading: Sample Notebook - Part 2
Reading: Assignment - Part 3
Reading: Sample Notebook - Part 3
Reading: Sample Notebook in R
Reading: Week 4 R Content
Graded: Week 4 - Quiz 1
Graded: Week 4 - Quiz 2
Graded: Week 4 - Quiz 3

Module 5

Video: Beyond Moneyball: Run values and WAR Part 1
Video: Beyond Moneyball: Run values and WAR Part 2
Video: Beyond Moneyball: Run values and WAR Part 3
Video: Beyond Moneyball: Run values and WAR Part 4
Ungraded Lab: Lecture - From Run Expectancy to WAR
Reading: Moneyball Week 5 - Assignment Overview
Ungraded Lab: Assignment 5 Workspace
Reading: Assignment - Part 1
Reading: Sample Notebook - Part 1
Reading: Assignment - Part 2
Reading: Sample Notebook - Part 2
Reading: Assignment - Part 3
Reading: Sample Notebook - Part 3
Reading: Post-Course Survey
Reading: Week 5 R Content
Graded: Week 5 - Quiz 1
Graded: Week 5 - Quiz 2
Graded: Week 5 - Quiz 3

Grading Policy

There are 15 quizzes in this course that are equally weighted and consist of 100% of your grade. You must receive a 100% to pass the quizzes in this course. However, you have an unlimited number of attempts.

Stefan Szymanski

Stephen J. Galetti Professor of Sport Management

Course content developed by U-M faculty and managed by the university. Faculty titles and affiliations are updated periodically.

Intermediate Level

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

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.

4.5

45 Ratings from Coursera

Most Recent Reviews

Read all reviews

October 09, 2025

No adapted to non-American students, would require more contextual information about the rules of baseball to be fully understandable. Otherwise nice real life example of how statistics are used to improve team outcomes and economical interests.

October 26, 2023

Excellent course, really enjoyed it even as someone who doesn't follow baseball

September 24, 2023

Was a very nice course and enjoyed it a lot!

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 !!!

October 27, 2022

I could not have been more disappointed in this course. Its lack of depth, both mathematically and from a sports analysis perspective, shocked me. This course spends almost no time at all on WHY any of the statistics presented are used. Nor on how to develop models to evaluate sport performance. Rather, it focuses almost entirely on prescriptive steps on how to calculate basic stats models. And, given all the time that is spent just walking through the code, one might expect that it is instructive and well-written. Sadly, that is not the case. The code for the course DRAMATICALLY under uses the power of the libraries included (Pandas and Numpy). Effectively, the first 4 weeks of the course are just tediously walking through dozens of convoluted lines of code, to do something that could have been 2 lines by simply using Pandas built-in functions. So, if you are interested in learning about statistical analysis methods for sports, you can literally watch the first video from week 5 and get 95% of the value of this course without having to slog through 4 weeks of (badly) coding up basically 2 regressions, only to prove that when two different people do the same math, they will arrive at the same result. If you are interested in learning how to use Python to do sports analysis, AVOID THIS CLASS COMPLETELY!! The lectures contain no information about how to apply any of what’s covered to anything outside of the one example given. AND, the code is so needlessly convoluted, it’s clear that whomever wrote it did NOT know how to use the tools available to them. So, if you are newer to Python, this course would be a little like trying to learn how to cook in an Italian style, by listening to someone read a recipe for canned spaghetti.

June 29, 2022

Some baseball concepts are complex for european people. But the content of the course is really interesting and very well explained.

Moneyball and Beyond

What You'll Learn