Former Lecturer of Sport Management
Prediction Models with Sports Data
What You'll Learn
- Learn how to generate forecasts of game results in professional sports using Python.
5 Modules
30 Hours
6 hrs per module (approx.)
Rating
About Prediction Models with Sports Data
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.
Skills You'll Gain
- Data Analysis
- Python (Programming Language)
- Sports Analytics
- Statistical Modeling
What You'll Earn
- Certificate of Completion:
- Certificates of completion acknowledge knowledge acquired upon completion of a non-credit course or program.
Welcome Message
Welcome to Prediction Models with Sports Data, a course focused on forecasting professional sports outcomes using Python. Learners apply logistic regression, evaluate betting odds, and explore ethical and social implications of sports analytics across multiple leagues.
This abbreviated syllabus description was created with the help of AI tools and reviewed by staff. The full syllabus is available to those who enroll in the course.
Course Schedule
Module 1
- Reading: Prediction Models Course Syllabus
- Reading: Help Us Learn More About You
- Video: Introduction to Prediction Models
- Video: Binary Outcome and Regression Part 1
- Video: Binary Outcome and Regression Part 2
- Video: Logistic Regression Part 1
- Video: Logistic Regression Part 2
- Video: Ordered Logistic Regression Part 1
- Video: Ordered Logistic Regression Part 2
- Video: Predictive Modeling - Basics of Forecasting
- Ungraded Lab: 1.1. LPM and Logit Model
- Ungraded Lab: 1.2. Ordered Logit Regression
- Ungraded Lab: 1.3. Predictive Modeling - Basics of Forecasting
- Ungraded Lab: Week 1 Self Test Solutions
- Reading: Assignment Overview
- Reading: Assignment Instructions - Part 1
- Ungraded Lab: Assignment 1 - Part 1 - Workspace
- Reading: Week 1 - Part 1 - Sample Notebook
- Reading: Assignment Instructions - Part 2
- Ungraded Lab: Assignment 1 - Part 2 - Workspace
- Reading: Week 1 - Part 2 - Sample Notebook
- Reading: Week 1 R Content
- Graded: Week 1 - Quiz 1
- Graded: Week 1 - Quiz 2
Module 2
- Video: Gambling and Betting Markets
- Video: Betting Odd and Types of Bets
- Video: Betting Odds and Win Probabilities
- Video: Evaluating Betting Odds Using Brier Scores Part 1
- Video: Evaluating Betting Odds Using Brier Scores Part 2
- Video: Market Efficiency and Beating the Bookmaker
- Ungraded Lab: 2.1. Betting Odds and Win Probabilities
- Ungraded Lab: 2.2. Evaluating Betting Odds Using Brier Scores
- Ungraded Lab: Self Test: Betting Odds and Win Probabilities
- Ungraded Lab: Self Test: Evaluating Betting Odds Using Brier Scores
- Reading: Assignment Overview
- Ungraded Lab: Assignment 2 Workspace
- Reading: Week 2 - Sample Notebook
- Reading: Week 2 R Content
- Graded: Week 2 Quiz
Module 3
- Video: Forecasting EPL results: 1. Wages and Transfermarket Part 1
- Video: Forecasting EPL results: 1. Wages and Transfermarket Part 2
- Video: Forecasting EPL results: Within sample prediction Part 1
- Video: Forecasting EPL results: Within sample prediction Part 2
- Video: Forecasting EPL results: Out of sample forecasting Part 1
- Video: Forecasting EPL results: Out of sample forecasting Part 2
- Video: Forecasting EPL results: Forecasting the League Table
- Ungraded Lab: 3.1. TMValues and Wages - 2011-2018
- Ungraded Lab: 3.2. Within Sample Predictions - Our Model VS The Bookmaker
- Ungraded Lab: 3.3. Forecasting EPL Results
- Ungraded Lab: 3.4. The forecast Premier League Table for 2019-20
- Ungraded Lab: Self Test: TMValues and Wages - 2011-2018
- Reading: Assignment Overview
- Ungraded Lab: Assignment 3 Workspace
- Reading: Week 3 - Sample Notebook
- Reading: Week 3 R Content
Module 4
- Video: Forecasting Model: MLB
- Video: Forecasting Model: NHL Part 1
- Video: Forecasting Model: NHL Part 2
- Video: Forecasting Model: NBA
- Ungraded Lab: 4.1. NHL Forecasting Model
- Ungraded Lab: 4.2. MLB Forecasting Model
- Ungraded Lab: 4.3. NBA Forecasting Model
- Reading: Assignment Overview
- Reading: Assignment Instructions
- Ungraded Lab: Assignment 4 Workspace
- Reading: Week 4 - Sample Notebooks
- Reading: Week 4 R Content
- Graded: Week 4 Quiz
Module 5
- Video: Gambling and the Development of Probability Theory
- Video: Gambling, Morality, and Sports Part 1
- Video: Gambling, Morality, and Sports Part 2
- Video: Social Policy and Sports Gambling
- Video: Problem Gambling Part 1
- Video: Problem Gambling Part 2
- Video: Match Fixing, Gambling and Sports
- Reading: Post-Course Survey
Grading Policy
Successful completion of all the assignments and tasks within the modules will result in a passing grade. Learners must receive 100% mastery (unlimited attempts) to pass the quizzes in this course. There are four quizzes in the course.
Stephen J. Galetti Professor of Sport Management
Course content developed by U-M faculty and managed by the university. Faculty titles and affiliations are updated periodically.
Intermediate Level
Learners should have some familiarity with Python before starting this course. We recommend the Python for Everybody Specialization.
