This course is part of the Sports Performance Analytics Specialization

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Christopher Brooks

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Sports Performance Analytics SpecializationUniversity of Michigan

About this Course

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In this course students will explore supervised machine learning techniques using the python scikit learn (sklearn) toolkit and real-world athletic data to understand both machine learning algorithms and how to predict athletic outcomes. Building on the previous courses in the specialization, students will apply methods such as support vector machines (SVM), decision trees, random forest, linear and logistic regression, and ensembles of learners to examine data from professional sports leagues such as the NHL and MLB as well as wearable devices such as the Apple Watch and inertial measurement units (IMUs). By the end of the course students will have a broad understanding of how classification and regression techniques can be used to enable sports analytics across athletic activities and events.

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Earn a Certificate upon completion

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Coursera Labs

Includes hands on learning projects.

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Course 5 of 5 in the

Sports Performance Analytics Specialization

Intermediate Level

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

Approx. 12 hours to complete

English

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Course 5 of 5 in the

Sports Performance Analytics Specialization

Intermediate Level

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

Approx. 12 hours to complete

English

Instructor

Christopher Brooks

Assistant Professor

School of Information

786,056 Learners

14 Courses

Offered by

University of Michigan

Syllabus - What you will learn from this course

Week1

Week 1

3 hours to complete

Machine Learning Concepts

3 hours to complete

7 videos (Total 75 min), 3 readings, 1 quiz

Week2

Week 2

4 hours to complete

Support Vector Machines

4 hours to complete

4 videos (Total 51 min), 2 readings, 1 quiz

Week3

Week 3

3 hours to complete

Decision Trees

3 hours to complete

4 videos (Total 58 min), 2 readings, 1 quiz

Week4

Week 4

3 hours to complete

Ensembles & Beyond

In this week of the course students will learn how many different models can be used together through ensembles, including the random forest method as a common use, as well as more general methods available in sklearn such as stacking and bagging. By the end of this week students will have a broad understanding of how methods such as SVMs, decision trees, and logistic regression can be used together to solve a problem with increasing performance.

3 hours to complete

5 videos (Total 102 min), 3 readings, 1 quiz

Reviews

4.5

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4 stars
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2 stars
7.69%

TOP REVIEWS FROM INTRODUCTION TO MACHINE LEARNING IN SPORTS ANALYTICS

by LROct 24, 2022

Very hands-on course, I could understand all techniques available to model sports.

by NMDec 4, 2022

Outstanding course! Really interesting and tutor was really enthusiastic which kept the videos and assessments easy to work through.

View all reviews

About the Sports Performance Analytics Specialization

Sports analytics has emerged as a field of research with increasing popularity propelled, in part, by the real-world success illustrated by the best-selling book and motion picture, Moneyball. Analysis of team and player performance data has continued to revolutionize the sports industry on the field, court, and ice as well as in living rooms among fantasy sports players and online sports gambling.

Drawing from real data sets in 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), you’ll learn how to construct predictive models to anticipate team and player performance. You’ll also replicate the success of Moneyball using real statistical models, use the Linear Probability Model (LPM) to anticipate categorical outcomes variables in sports contests, explore how teams collect and organize an athlete’s performance data with wearable technologies, and how to apply machine learning in a sports analytics context. This introduction to the field of sports analytics is designed for sports managers, coaches, physical therapists, as well as sports fans who want to understand the science behind athlete performance and game prediction. New Python programmers and data analysts who are looking for a fun and practical way to apply their Python, statistics, or predictive modeling skills will enjoy exploring courses in this series.

Frequently Asked Questions

When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

More questions? Visit the Learner Help Center.

Introduction to Machine Learning in Sports Analytics

About this Course