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Moneyball and Beyond

Moneyball and Beyond

This course is part of Sports Performance Analytics Specialization

Instructor: Stefan Szymanski

4,916 already enrolled

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5 modules

Gain insight into a topic and learn the fundamentals.

53 reviews

Intermediate level

Recommended experience

3 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

5 modules

Gain insight into a topic and learn the fundamentals.

53 reviews

Intermediate level

Recommended experience

3 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

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.

Skills you'll gain

Tools you'll learn

Python Programming

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

15 assignments

Taught in English

91%

of learners achieved a positive career outcome

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Build your subject-matter expertise

This course is part of the Sports Performance Analytics Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 5 modules in this course

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.

Module details

In this module we introduce the Moneyball story and explore the method used to test that story. We begin the process of replicating the moneyball test by establishing the relationship between team winning and and two performance statistics - on base percentage (OBP) and slugging percentage (SLG).

What's included

5 videos10 readings3 assignments2 ungraded labs

5 videosTotal 61 minutes

Introduction to Moneyball7 minutes
Reproducing Table 1 of Hakes and Sauer - Part 119 minutes
Reproducing Table 1 of Hakes and Sauer - Part 213 minutes
Reproducing Table 1 of Hakes and Sauer- Part 313 minutes
Reproducing Table 1 of Hakes and Sauer - Part 410 minutes

10 readingsTotal 95 minutes

Course Syllabus10 minutes
Help Us Learn More About You5 minutes
Week 1 - Assignment Overview10 minutes
Assignment - Part 110 minutes
Sample Notebook - Part 110 minutes
Assignment - Part 210 minutes
Sample Notebook - Part 210 minutes
Assignment - Part 310 minutes
Sample Notebook - Full Sample10 minutes
Week 1 R Content10 minutes

3 assignmentsTotal 90 minutes

Week 1 - Quiz 130 minutes
Week 1 - Quiz 230 minutes
Week 1 - Quiz 330 minutes

2 ungraded labsTotal 120 minutes

Lecture - H&S Table60 minutes
Assignment 1 Workspace60 minutes

In this module we estimate the relationship between MLB player salaries and their performance statistics, OBP (on base percentage) and SLG (slugging). The results appear to confirm the Moneyball story - OBP was undervalued relative to SLG prior to the publication of Moneyball, while after publication the relative significance is reversed.

What's included

6 videos8 readings3 assignments2 ungraded labs

6 videosTotal 57 minutes

Reproducing Table 3 of Hakes and Sauer- Part 19 minutes
Reproducing Table 3 of Hakes and Sauer- Part 26 minutes
Reproducing Table 3 of Hakes and Sauer- Part 310 minutes
Reproducing Table 3 of Hakes and Sauer- Part 411 minutes
Reproducing Table 3 of Hakes and Sauer- Part 510 minutes
Reproducing Table 3 of Hakes and Sauer- Part 611 minutes

8 readingsTotal 80 minutes

Moneyball Week 2 - Assignment Overview10 minutes
Assignment - Part 110 minutes
Sample Notebook - Part 110 minutes
Assignment - Part 210 minutes
Sample Notebook - Part 210 minutes
Assignment - Part 310 minutes
Sample Notebook - Full Sample10 minutes
Week 2 R Content10 minutes

3 assignmentsTotal 90 minutes

Week 2 - Quiz 130 minutes
Week 2 - Quiz 230 minutes
Week 2 - Quiz 330 minutes

2 ungraded labsTotal 120 minutes

Lecture - Moneyball Table 360 minutes
Assignment 2 Workspace60 minutes

This module updates the analysis of Hakes & Sauer and estimates the rewards to OBP and SLG over the period 1994 -2015. In addition it shows how rewards can be related to individual components of SLG: walks, singles, doubles, triples, and home runs.

What's included

6 videos9 readings3 assignments2 ungraded labs

6 videosTotal 55 minutes

Moneyball update Part 15 minutes
Moneyball update Part 27 minutes
Moneyball Update Part 39 minutes
Moneyball Update Part 412 minutes
Moneyball Update Part 58 minutes
Moneyball Update Part 613 minutes

9 readingsTotal 90 minutes

Moneyball Week 3 - Assignment Overview10 minutes
Assignment - Part 110 minutes
Sample Notebook - Part 110 minutes
Assignment - Part 210 minutes
Sample Notebook - Part 210 minutes
Assignment - Part 310 minutes
Sample Notebook - Part 310 minutes
Sample Notebook in R10 minutes
Week 3 R Content10 minutes

3 assignmentsTotal 66 minutes

Week 3 - Quiz 130 minutes
Week 3 - Quiz 26 minutes
Week 3 - Quiz 330 minutes

2 ungraded labsTotal 120 minutes

Lecture - Moneyball Update60 minutes
Assignment 3 Workspace60 minutes

This module introduces the concept of run expectancy, shows how to derive the run expectancy matrix and the calculation of run values based on an MLB dataset of all events in the 2018 season. Run values are calculated by event type (walks, singles, doubles, etc.) and by player.

What's included

4 videos9 readings3 assignments2 ungraded labs

4 videosTotal 44 minutes

Beyond Moneyball: Run expectancy Part 111 minutes
Beyond Moneyball: Run Expectancy Part 212 minutes
Beyond Moneyball: Run expectancy Part 312 minutes
Beyond Moneyball: Run expectancy Part 49 minutes

9 readingsTotal 90 minutes

Moneyball Week 4 - Assignment Overview10 minutes
Assignment - Part 110 minutes
Sample Notebook - Part 110 minutes
Assignment - Part 210 minutes
Sample Notebook - Part 210 minutes
Assignment - Part 310 minutes
Sample Notebook - Part 310 minutes
Sample Notebook in R10 minutes
Week 4 R Content10 minutes

3 assignmentsTotal 90 minutes

Week 4 - Quiz 130 minutes
Week 4 - Quiz 230 minutes
Week 4 - Quiz 330 minutes

2 ungraded labsTotal 120 minutes

Lecture - Run Expectancy60 minutes
Assignment 4 Workspace60 minutes

This module examines the concept of Wins Above Replacement (WAR) and shows how to calculate WAR based on batting performance. The relationship between play run values team win percentage and player salaries is then explored. Run values are shown to have a high degree of correlation with winning and with salaries. Run values can to a limited extent predict win percentage.