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

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

This course is part of Sports Performance Analytics Specialization

Taught in English

Some content may not be translated

Wenche Wang
Stefan Szymanski

Instructors: Wenche Wang

18,465 already enrolled

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Course

Gain insight into a topic and learn the fundamentals

4.4

(161 reviews)

Intermediate level

Recommended experience

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

What you'll learn

  • Use Python to analyze team performance in sports.

  • Become a producer of sports analytics rather than a consumer.

Details to know

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Assessments

13 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.4

(161 reviews)

Intermediate level

Recommended experience

49 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.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
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There are 6 modules in this course

This week introduces a simple example of sports analytics in practice - the calculation of the Pythagorean expectation to model winning in team sports. This can also be used for the purposes of prediction. Examples are developed for five different sports leagues, Major League Baseball (MLB), the National Basketball Association (NBA), the National Hockey League (NHL), the English Premier League (EPL-soccer) and the Indian Premier League (IPL-cricket).

What's included

8 videos6 readings1 quiz7 ungraded labs

This week will use NBA data to introduce basic and important Python codes to conduct data cleaning and data preparation. This week also discusses summary and descriptive analyses with statistics and graphs to understand the distribution of data, the characteristics and pattern of variables as well as the relationship between two variables. At the end of this week, we will introduce correlation coefficients to summarize the linear relationship between two variables.

What's included

6 videos6 readings3 quizzes5 ungraded labs

This module introduces some ways of representing data using examples from MLB, the NBA and Indian Premier League. MLB data is used to analyze the spatial distribution of different hits. NBA data is used to generate heatmaps to illustrate the different ways in which players contribute. IPL data is used to show how team performances can be compared graphically.

What's included

4 videos6 readings2 quizzes5 ungraded labs

This week introduces the fundamentals of regression analysis. We will discuss how to perform regression analysis using Python and how to interpret regression output. We will use NHL data to estimate multiple regression models to identify the team level performance factors that affect the team's winning percentage. We will also use cricket data from the Indian Premier League to run regression analyses to examine whether player performance impacts player salary.

What's included

6 videos6 readings3 quizzes4 ungraded labs

This module uses regression analysis to investigate the relationship between team salary spending and team performance in the NBA, NHL, EPL and IPL. The module explores different ways of defining the regression model, and how to interpret competing regression model results.

What's included

4 videos4 readings1 quiz5 ungraded labs

This week studies an interesting topic in sport, the hot hand. We will introduce the concept of hot hand and discuss the academic research that examines whether the hot hand is a phenomenon or a fallacy. We will demonstrate how to analytically test the hot hand using the NBA shot log data. We will test whether NBA players have hot hand by computing conditional probabilities and autocorrelation coefficients as well as performing regression analyses.

What's included

8 videos7 readings3 quizzes5 ungraded labs

Instructors

Instructor ratings
4.3 (49 ratings)
Wenche Wang
University of Michigan
1 Course18,465 learners
Stefan Szymanski
University of Michigan
3 Courses20,747 learners

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