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Introduction to Machine Learning in Sports Analytics

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Introduction to Machine Learning in Sports Analytics

This course is part of Sports Performance Analytics Specialization

Instructor: Christopher Brooks

5,435 already enrolled

Included with

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4 modules

Gain insight into a topic and learn the fundamentals.

27 reviews

Intermediate level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

27 reviews

Intermediate level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

Gain an understanding of how classification and regression techniques can be used to enable sports analytics across athletic activities and events.

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

4 assignments

Taught in English

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This course is part of the Sports Performance Analytics Specialization

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There are 4 modules in this course

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.

Module details

This week will introduce the concept of machine learning and describe the four major areas of places it can be used in sports analytics. The machine learning pipeline will be discussed, as well as some common issues one runs into when using machine learning for sports analytics.

What's included

7 videos3 readings1 assignment1 ungraded lab

7 videosTotal 75 minutes

Introduction3 minutes
What is Machine Learning?8 minutes
The Machine Learning Workflow16 minutes
Our First Model: NHL Game Outcomes20 minutes
Building the Logistic Regression Model6 minutes
Considerations in Deploying The Model20 minutes
Wrap Up1 minute

3 readingsTotal 30 minutes

Help Us Learn More About You10 minutes
Course Syllabus10 minutes
Assignment 1 Programming Solution10 minutes

1 assignmentTotal 60 minutes

Assignment 160 minutes

1 ungraded labTotal 10 minutes

JupyterLab10 minutes

In this week students will learn how Support Vector Machines (SVM) work, and will experience these models when looking at both baseball and wearable data. Coming out of the week students will have experience building SVMs with real data and will be able to apply them to problems of their own.

What's included

4 videos2 readings1 assignment

4 videosTotal 51 minutes

Introduction to Support Vector Machines (SVMs)16 minutes
Polynomial Support Vector Machines11 minutes
Cross Validation9 minutes
A Real World SVM Model: Boxing Punch Classification15 minutes

2 readingsTotal 130 minutes

(Optional) - An evaluation of wearable inertial sensor configuration and supervised machine learning models for automatic punch classification in boxing120 minutes
Assignment 2 Programming Solution10 minutes

1 assignmentTotal 60 minutes

Assignment 260 minutes

This week will focus on interpretable methods for machine learning with a particular focus on decision trees. Students will learn how these models work in general, and see special uses of decision trees in combination with regression methods. In this week students will come to better understand how the python sklearn toolkit can be used for a breadth of supervised learning tasks.

What's included

4 videos2 readings1 assignment

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.

What's included

5 videos3 readings1 assignment

5 videosTotal 102 minutes

Ensembles23 minutes
Additional Machine Learning Concepts5 minutes
Baseball Hall of Fame Prediction15 minutes
Baseball Hall of Fame Demonstration Part 1 23 minutes
Baseball Hall of Fame Demonstration Part 2 36 minutes

3 readingsTotal 30 minutes

Free Deepnote Notebook Service10 minutes
Putting Your Skills to the Test!10 minutes
Post Course Survey10 minutes

1 assignmentTotal 30 minutes

Assignment 430 minutes

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Instructor

Instructor ratings

(6 ratings)

Christopher Brooks

15 Courses963,881 learners

Offered by

University of Michigan

Learner reviews

5 stars
81.48%
4 stars
11.11%
3 stars
3.70%
2 stars
3.70%
1 star
0%

Showing 3 of 27

Reviewed on Oct 24, 2022

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

Reviewed on May 6, 2023

Well-structured notebook, resourceful, applicable to real-world projects, clear and entertaining teaching. Highly satisfied. One of the best modules in the entire specialization.

Reviewed on Oct 30, 2024

Provide solid foundation for beginning supervised ML

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