This course is part of the Sports Performance Analytics Specialization

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Sports Performance Analytics SpecializationUniversity of Michigan

About this Course

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In this course the learner will be shown how to generate forecasts of game results in professional sports using Python. The main emphasis of the course is on teaching the method of logistic regression as a way of modeling game results, using data on team expenditures. The learner is taken through the process of modeling past results, and then using the model to forecast the outcome games not yet played. The course will show the learner how to evaluate the reliability of a model using data on betting odds. The analysis is applied first to the English Premier League, then the NBA and NHL. The course also provides an overview of the relationship between data analytics and gambling, its history and the social issues that arise in relation to sports betting, including the personal risks.

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Shareable Certificate

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Coursera Labs

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Course 3 of 5 in the

Sports Performance Analytics Specialization

Approx. 33 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 3 of 5 in the

Sports Performance Analytics Specialization

Approx. 33 hours to complete

English

Instructors

Youngho Park

Lecturer of Sport Management

School of Kinesiology

3,271 Learners

1 Course

Stefan Szymanski

Stephen J. Galetti Professor of Sport Management

School of Kinesiology

14,684 Learners

3 Courses

Offered by

University of Michigan

Syllabus - What you will learn from this course

Week1

Week 1

10 hours to complete

Week 1

This module introduces the regression models in dealing with the categorical outcome variables in sport contest (i.e., Win, Draw, Lose). It explains the Linear Probability Model (LPM) in terms of its theoretical foundations, computational applications, and empirical limitations. Then the module introduces and demonstrates the Logistic Regression as a better substitute of LPM for the categorical dependent variables.

10 hours to complete

8 videos (Total 86 min), 8 readings, 2 quizzes

8 videos

Introduction to Prediction Models2mBinary Outcome and Regression Part 18mBinary Outcome and Regression Part 211mLogistic Regression Part 111mLogistic Regression Part 215mOrdered Logistic Regression Part 16mOrdered Logistic Regression Part 212mPredictive Modeling - Basics of Forecasting16m

8 readings

Prediction Models Course Syllabus10mHelp Us Learn More About You5mAssignment Overview10mAssignment Instructions - Part 110mWeek 1 - Part 1 - Sample Notebook10mAssignment Instructions - Part 210mWeek 1 - Part 2 - Sample Notebook10mWeek 1 R Content10m

2 practice exercises

Week 1 - Quiz 130mWeek 1 - Quiz 230m

Week2

Week 2

7 hours to complete

Week 2

7 hours to complete

6 videos (Total 89 min), 3 readings, 1 quiz

Week3

Week 3

9 hours to complete

Week 3

9 hours to complete

7 videos (Total 93 min), 3 readings, 1 quiz

7 videos

Forecasting EPL results: 1. Wages and Transfermarket Part 18mForecasting EPL results: 1. Wages and Transfermarket Part 212mForecasting EPL results: Within sample prediction Part 117mForecasting EPL results: Within sample prediction Part 211mForecasting EPL results: Out of sample forecasting Part 115mForecasting EPL results: Out of sample forecasting Part 214mForecasting EPL results: Forecasting the League Table14m

3 readings

Assignment Overview10mWeek 3 - Sample Notebook10mWeek 3 R Content10m

1 practice exercise

Week 3 Quiz30m

Week4

Week 4

6 hours to complete

Week 4

This module assesses the efficacy of the EPL forecasting model covered in the previous week by replicating the model in the context of three North American team sports leagues (i.e., NHL, NBA, MLB). Specifically, this module shows how to forecast the outcome of NHL, NBA, MLB regular season games using an ordered logit model and publicly available information. It assesses the accuracy of these forecasts against the betting odds.

6 hours to complete

4 videos (Total 69 min), 4 readings, 1 quiz

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

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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.
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Prediction Models with Sports Data

About this Course