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

6,947 already enrolled

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5 modules

Gain insight into a topic and learn the fundamentals.

41 reviews

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.

41 reviews

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

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

5 assignments

Taught in English

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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.

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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 videos Total 86 minutes

Introduction to Prediction Models 2 minutes
Binary Outcome and Regression Part 1 9 minutes
Binary Outcome and Regression Part 2 12 minutes
Logistic Regression Part 1 12 minutes
Logistic Regression Part 2 16 minutes
Ordered Logistic Regression Part 1 7 minutes
Ordered Logistic Regression Part 2 12 minutes
Predictive Modeling - Basics of Forecasting 17 minutes

8 readings Total 75 minutes

Prediction Models Course Syllabus 10 minutes
Help Us Learn More About You 5 minutes
Assignment Overview 10 minutes
Assignment Instructions - Part 1 10 minutes
Week 1 - Part 1 - Sample Notebook 10 minutes
Assignment Instructions - Part 2 10 minutes
Week 1 - Part 2 - Sample Notebook 10 minutes
Week 1 R Content 10 minutes

2 assignments Total 60 minutes

Week 1 - Quiz 1 30 minutes
Week 1 - Quiz 2 30 minutes

6 ungraded labs Total 360 minutes

1.1. LPM and Logit Model 60 minutes
1.2. Ordered Logit Regression 60 minutes
1.3. Predictive Modeling - Basics of Forecasting 60 minutes
Week 1 Self Test Solutions 60 minutes
Assignment 1 - Part 1 - Workspace 60 minutes
Assignment 1 - Part 2 - Workspace 60 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 videos Total 89 minutes

Gambling and Betting Markets 19 minutes
Betting Odd and Types of Bets 17 minutes
Betting Odds and Win Probabilities 20 minutes
Evaluating Betting Odds Using Brier Scores Part 1 8 minutes
Evaluating Betting Odds Using Brier Scores Part 2 12 minutes
Market Efficiency and Beating the Bookmaker 13 minutes

3 readings Total 30 minutes

Assignment Overview 10 minutes
Week 2 - Sample Notebook 10 minutes
Week 2 R Content 10 minutes

1 assignment Total 30 minutes

Week 2 Quiz 30 minutes

5 ungraded labs Total 300 minutes

2.1. Betting Odds and Win Probabilities 60 minutes
2.2. Evaluating Betting Odds Using Brier Scores 60 minutes
Self Test: Betting Odds and Win Probabilities 60 minutes
Self Test: Evaluating Betting Odds Using Brier Scores 60 minutes
Assignment 2 Workspace 60 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 videos Total 93 minutes

Forecasting EPL results: 1. Wages and Transfermarket Part 1 8 minutes
Forecasting EPL results: 1. Wages and Transfermarket Part 2 12 minutes
Forecasting EPL results: Within sample prediction Part 1 17 minutes
Forecasting EPL results: Within sample prediction Part 2 12 minutes
Forecasting EPL results: Out of sample forecasting Part 1 16 minutes
Forecasting EPL results: Out of sample forecasting Part 2 14 minutes
Forecasting EPL results: Forecasting the League Table 14 minutes

3 readings Total 30 minutes

Assignment Overview 10 minutes
Week 3 - Sample Notebook 10 minutes
Week 3 R Content 10 minutes

1 assignment Total 30 minutes

Week 3 Quiz 30 minutes

6 ungraded labs Total 360 minutes

3.1. TMValues and Wages - 2011-2018 60 minutes
3.2. Within Sample Predictions - Our Model VS The Bookmaker 60 minutes
3.3. Forecasting EPL Results 60 minutes
3.4. The forecast Premier League Table for 2019-20 60 minutes
Self Test: TMValues and Wages - 2011-2018 60 minutes
Assignment 3 Workspace 60 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 videos Total 69 minutes

Forecasting Model: MLB 20 minutes
Forecasting Model: NHL Part 1 17 minutes
Forecasting Model: NHL Part 2 7 minutes
Forecasting Model: NBA 26 minutes

4 readings Total 40 minutes

Assignment Overview 10 minutes
Assignment Instructions 10 minutes
Week 4 - Sample Notebooks 10 minutes
Week 4 R Content 10 minutes

1 assignment Total 30 minutes

Week 4 Quiz 30 minutes

4 ungraded labs Total 240 minutes

4.1. NHL Forecasting Model 60 minutes
4.2. MLB Forecasting Model 60 minutes
4.3. NBA Forecasting Model 60 minutes
Assignment 4 Workspace 60 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 videos Total 81 minutes

Gambling and the Development of Probability Theory 17 minutes
Gambling, Morality, and Sports Part 1 14 minutes
Gambling, Morality, and Sports Part 2 8 minutes
Social Policy and Sports Gambling 13 minutes
Problem Gambling Part 1 7 minutes
Problem Gambling Part 2 10 minutes
Match Fixing, Gambling and Sports 13 minutes

1 reading Total 5 minutes

Post-Course Survey 5 minutes

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Instructors

Youngho Park

University of Michigan

1 Course 6,947 learners

Stefan Szymanski

University of Michigan

3 Courses 31,485 learners

Offered by

University of Michigan

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Reviewed on Jul 10, 2023

I found the material from weeks 2 and 4 very interesting!

Reviewed on Apr 11, 2024

Very interesting course, even though some of the data prep is kind of weird it's nice to see things done a bit differently

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