Prediction Models with Sports Data

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Prediction Models with Sports Data

This course is part of Sports Performance Analytics Specialization

Instructors: Youngho Park

7,242 already enrolled

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5 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

3 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

5 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

3 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

Learn how to generate forecasts of game results in professional sports using Python.

Skills you'll gain

Tools you'll learn

Python Programming

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

5 assignments

Taught in English

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Build your subject-matter expertise

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
Earn a shareable career certificate

There are 5 modules in this course

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.

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.

What's included

8 videos8 readings2 assignments6 ungraded labs

8 videosTotal 86 minutes

Introduction to Prediction Models2 minutes
Binary Outcome and Regression Part 19 minutes
Binary Outcome and Regression Part 212 minutes
Logistic Regression Part 112 minutes
Logistic Regression Part 216 minutes
Ordered Logistic Regression Part 17 minutes
Ordered Logistic Regression Part 212 minutes
Predictive Modeling - Basics of Forecasting17 minutes

8 readingsTotal 75 minutes

Prediction Models Course Syllabus10 minutes
Help Us Learn More About You5 minutes
Assignment Overview10 minutes
Assignment Instructions - Part 110 minutes
Week 1 - Part 1 - Sample Notebook10 minutes
Assignment Instructions - Part 210 minutes
Week 1 - Part 2 - Sample Notebook10 minutes
Week 1 R Content10 minutes

2 assignmentsTotal 60 minutes

Week 1 - Quiz 130 minutes
Week 1 - Quiz 230 minutes

6 ungraded labsTotal 360 minutes

1.1. LPM and Logit Model60 minutes
1.2. Ordered Logit Regression60 minutes
1.3. Predictive Modeling - Basics of Forecasting60 minutes
Week 1 Self Test Solutions60 minutes
Assignment 1 - Part 1 - Workspace60 minutes
Assignment 1 - Part 2 - Workspace60 minutes

This module explores the relationship between probability and betting markets. It explains the concept of odds, and the relationship between betting odds and probabilities. It then develops a measure of the accuracy of betting odds using sports examples, and assesses the meaning of efficiency in betting markets.

What's included

6 videos3 readings1 assignment5 ungraded labs

6 videosTotal 89 minutes

Gambling and Betting Markets19 minutes
Betting Odd and Types of Bets17 minutes
Betting Odds and Win Probabilities20 minutes
Evaluating Betting Odds Using Brier Scores Part 18 minutes
Evaluating Betting Odds Using Brier Scores Part 212 minutes
Market Efficiency and Beating the Bookmaker13 minutes

3 readingsTotal 30 minutes

Assignment Overview10 minutes
Week 2 - Sample Notebook10 minutes
Week 2 R Content10 minutes

1 assignmentTotal 30 minutes

Week 2 Quiz30 minutes

5 ungraded labsTotal 300 minutes

2.1. Betting Odds and Win Probabilities60 minutes
2.2. Evaluating Betting Odds Using Brier Scores60 minutes
Self Test: Betting Odds and Win Probabilities60 minutes
Self Test: Evaluating Betting Odds Using Brier Scores60 minutes
Assignment 2 Workspace60 minutes

This module shows how to forecast the outcome of EPL soccer games using an ordered logit model and publicly available information. It assesses the accuracy of these forecasts against the betting odds and shows that they are remarkably accurate.

What's included

7 videos3 readings1 assignment6 ungraded labs

7 videosTotal 93 minutes

Forecasting EPL results: 1. Wages and Transfermarket Part 18 minutes
Forecasting EPL results: 1. Wages and Transfermarket Part 212 minutes
Forecasting EPL results: Within sample prediction Part 117 minutes
Forecasting EPL results: Within sample prediction Part 212 minutes
Forecasting EPL results: Out of sample forecasting Part 116 minutes
Forecasting EPL results: Out of sample forecasting Part 214 minutes
Forecasting EPL results: Forecasting the League Table14 minutes

3 readingsTotal 30 minutes

Assignment Overview10 minutes
Week 3 - Sample Notebook10 minutes
Week 3 R Content10 minutes

1 assignmentTotal 30 minutes

Week 3 Quiz30 minutes

6 ungraded labsTotal 360 minutes

3.1. TMValues and Wages - 2011-201860 minutes
3.2. Within Sample Predictions - Our Model VS The Bookmaker60 minutes
3.3. Forecasting EPL Results60 minutes
3.4. The forecast Premier League Table for 2019-2060 minutes
Self Test: TMValues and Wages - 2011-201860 minutes
Assignment 3 Workspace60 minutes

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.

What's included

4 videos4 readings1 assignment4 ungraded labs

4 videosTotal 69 minutes

Forecasting Model: MLB20 minutes
Forecasting Model: NHL Part 117 minutes
Forecasting Model: NHL Part 27 minutes
Forecasting Model: NBA26 minutes

4 readingsTotal 40 minutes

Assignment Overview10 minutes
Assignment Instructions10 minutes
Week 4 - Sample Notebooks10 minutes
Week 4 R Content10 minutes

1 assignmentTotal 30 minutes

Week 4 Quiz30 minutes

4 ungraded labsTotal 240 minutes

4.1. NHL Forecasting Model60 minutes
4.2. MLB Forecasting Model60 minutes
4.3. NBA Forecasting Model60 minutes
Assignment 4 Workspace60 minutes

In this module we examine the historical and social consequences of gambling, and the relationship between gambling and statistics. Gambling is explored from the perspective of different ethical and religious systems. Issues of problem gambling are explored and assessed.

What's included

7 videos1 reading

7 videosTotal 81 minutes

Gambling and the Development of Probability Theory17 minutes
Gambling, Morality, and Sports Part 114 minutes
Gambling, Morality, and Sports Part 28 minutes
Social Policy and Sports Gambling 13 minutes
Problem Gambling Part 17 minutes
Problem Gambling Part 210 minutes
Match Fixing, Gambling and Sports13 minutes

1 readingTotal 5 minutes

Post-Course Survey5 minutes

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Instructors

Youngho Park

University of Michigan

1 Course7,247 learners

Stefan Szymanski

University of Michigan

3 Courses33,238 learners

Offered by

University of Michigan

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