Moneyball and Beyond

Moneyball and Beyond

This course is part of Sports Performance Analytics Specialization

Instructor: Stefan Szymanski

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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

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This course is part of the Sports Performance Analytics Specialization

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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.

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 videos Total 61 minutes

Introduction to Moneyball 7 minutes
Reproducing Table 1 of Hakes and Sauer - Part 1 19 minutes
Reproducing Table 1 of Hakes and Sauer - Part 2 13 minutes
Reproducing Table 1 of Hakes and Sauer- Part 3 13 minutes
Reproducing Table 1 of Hakes and Sauer - Part 4 10 minutes

10 readings Total 95 minutes

Course Syllabus 10 minutes
Help Us Learn More About You 5 minutes
Week 1 - Assignment Overview 10 minutes
Assignment - Part 1 10 minutes
Sample Notebook - Part 1 10 minutes
Assignment - Part 2 10 minutes
Sample Notebook - Part 2 10 minutes
Assignment - Part 3 10 minutes
Sample Notebook - Full Sample 10 minutes
Week 1 R Content 10 minutes

3 assignments Total 90 minutes

Week 1 - Quiz 1 30 minutes
Week 1 - Quiz 2 30 minutes
Week 1 - Quiz 3 30 minutes

2 ungraded labs Total 120 minutes

Lecture - H&S Table 60 minutes
Assignment 1 Workspace 60 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 videos Total 57 minutes

Reproducing Table 3 of Hakes and Sauer- Part 1 9 minutes
Reproducing Table 3 of Hakes and Sauer- Part 2 6 minutes
Reproducing Table 3 of Hakes and Sauer- Part 3 10 minutes
Reproducing Table 3 of Hakes and Sauer- Part 4 11 minutes
Reproducing Table 3 of Hakes and Sauer- Part 5 10 minutes
Reproducing Table 3 of Hakes and Sauer- Part 6 11 minutes

8 readings Total 80 minutes

Moneyball Week 2 - Assignment Overview 10 minutes
Assignment - Part 1 10 minutes
Sample Notebook - Part 1 10 minutes
Assignment - Part 2 10 minutes
Sample Notebook - Part 2 10 minutes
Assignment - Part 3 10 minutes
Sample Notebook - Full Sample 10 minutes
Week 2 R Content 10 minutes

3 assignments Total 90 minutes

Week 2 - Quiz 1 30 minutes
Week 2 - Quiz 2 30 minutes
Week 2 - Quiz 3 30 minutes

2 ungraded labs Total 120 minutes

Lecture - Moneyball Table 3 60 minutes
Assignment 2 Workspace 60 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 videos Total 55 minutes

Moneyball update Part 1 5 minutes
Moneyball update Part 2 7 minutes
Moneyball Update Part 3 9 minutes
Moneyball Update Part 4 12 minutes
Moneyball Update Part 5 8 minutes
Moneyball Update Part 6 13 minutes

9 readings Total 90 minutes

Moneyball Week 3 - Assignment Overview 10 minutes
Assignment - Part 1 10 minutes
Sample Notebook - Part 1 10 minutes
Assignment - Part 2 10 minutes
Sample Notebook - Part 2 10 minutes
Assignment - Part 3 10 minutes
Sample Notebook - Part 3 10 minutes
Sample Notebook in R 10 minutes
Week 3 R Content 10 minutes

3 assignments Total 66 minutes

Week 3 - Quiz 1 30 minutes
Week 3 - Quiz 2 6 minutes
Week 3 - Quiz 3 30 minutes

2 ungraded labs Total 120 minutes

Lecture - Moneyball Update 60 minutes
Assignment 3 Workspace 60 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 videos Total 44 minutes

Beyond Moneyball: Run expectancy Part 1 11 minutes
Beyond Moneyball: Run Expectancy Part 2 12 minutes
Beyond Moneyball: Run expectancy Part 3 12 minutes
Beyond Moneyball: Run expectancy Part 4 9 minutes

9 readings Total 90 minutes

Moneyball Week 4 - Assignment Overview 10 minutes
Assignment - Part 1 10 minutes
Sample Notebook - Part 1 10 minutes
Assignment - Part 2 10 minutes
Sample Notebook - Part 2 10 minutes
Assignment - Part 3 10 minutes
Sample Notebook - Part 3 10 minutes
Sample Notebook in R 10 minutes
Week 4 R Content 10 minutes

3 assignments Total 90 minutes

Week 4 - Quiz 1 30 minutes
Week 4 - Quiz 2 30 minutes
Week 4 - Quiz 3 30 minutes

2 ungraded labs Total 120 minutes

Lecture - Run Expectancy 60 minutes
Assignment 4 Workspace 60 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.