University of Michigan

Moneyball and Beyond

This course is part of Sports Performance Analytics Specialization

Taught in English

Some content may not be translated

Stefan Szymanski

Instructor: Stefan Szymanski

3,282 already enrolled

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Course

Gain insight into a topic and learn the fundamentals

4.6

(46 reviews)

Intermediate level

Recommended experience

28 hours (approximately)
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.

Details to know

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Assessments

15 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.6

(46 reviews)

Intermediate level

Recommended experience

28 hours (approximately)
Flexible schedule
Learn at your own pace

See how employees at top companies are mastering in-demand skills

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This course is part of the Sports Performance Analytics Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 5 modules in this course

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 quizzes2 ungraded labs

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 quizzes2 ungraded labs

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 quizzes2 ungraded labs

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 quizzes2 ungraded labs

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.

What's included

4 videos9 readings3 quizzes2 ungraded labs

Instructor

Instructor ratings
4.7 (13 ratings)
Stefan Szymanski
University of Michigan
3 Courses21,004 learners

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4.6

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