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.
Monthly Subscription Starting at AED 99 + VAT
- Level Foundation
- Duration 34 hours
- Course by University of Michigan
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Offered by
About
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.Modules
Week 1: Lecture Videos
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8
Videos
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2
Readings
8
Videos
- Introduction to Prediction Models
- Binary Outcome and Regression Part 1
- Binary Outcome and Regression Part 2
- Logistic Regression Part 1
- Logistic Regression Part 2
- Ordered Logistic Regression Part 1
- Ordered Logistic Regression Part 2
- Predictive Modeling - Basics of Forecasting
2
Readings
- Prediction Models Course Syllabus
- Help Us Learn More About You
Week 1: Lecture Notebooks
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4
Lab
4
Labs
- 1.1. LPM and Logit Model
- 1.2. Ordered Logit Regression
- 1.3. Predictive Modeling - Basics of Forecasting
- Week 1 Self Test Solutions
Week 1: Assignment
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5
Readings
-
2
Lab
-
2
Assignment
2
Assignment
- Week 1 - Quiz 1
- Week 1 - Quiz 2
2
Labs
- Assignment 1 - Part 1 - Workspace
- Assignment 1 - Part 2 - Workspace
5
Readings
- Assignment Overview
- Assignment Instructions - Part 1
- Week 1 - Part 1 - Sample Notebook
- Assignment Instructions - Part 2
- Week 1 - Part 2 - Sample Notebook
Week 1: R Content
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1
Readings
1
Readings
- Week 1 R Content
Week 2: Lecture Videos
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6
Videos
6
Videos
- Gambling and Betting Markets
- Betting Odd and Types of Bets
- Betting Odds and Win Probabilities
- Evaluating Betting Odds Using Brier Scores Part 1
- Evaluating Betting Odds Using Brier Scores Part 2
- Market Efficiency and Beating the Bookmaker
Week 2: Lecture Notebooks
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4
Lab
4
Labs
- 2.1. Betting Odds and Win Probabilities
- 2.2. Evaluating Betting Odds Using Brier Scores
- Self Test: Betting Odds and Win Probabilities
- Self Test: Evaluating Betting Odds Using Brier Scores
Week 2: Assignment
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2
Readings
-
1
Lab
-
1
Assignment
1
Assignment
- Week 2 Quiz
1
Labs
- Assignment 2 Workspace
2
Readings
- Assignment Overview
- Week 2 - Sample Notebook
Week 2: R Content
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1
Readings
1
Readings
- Week 2 R Content
Week 3: Lecture Videos
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7
Videos
7
Videos
- Forecasting EPL results: 1. Wages and Transfermarket Part 1
- Forecasting EPL results: 1. Wages and Transfermarket Part 2
- Forecasting EPL results: Within sample prediction Part 1
- Forecasting EPL results: Within sample prediction Part 2
- Forecasting EPL results: Out of sample forecasting Part 1
- Forecasting EPL results: Out of sample forecasting Part 2
- Forecasting EPL results: Forecasting the League Table
Week 3: Lecture Notebooks
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5
Lab
5
Labs
- 3.1. TMValues and Wages - 2011-2018
- 3.2. Within Sample Predictions - Our Model VS The Bookmaker
- 3.3. Forecasting EPL Results
- 3.4. The forecast Premier League Table for 2019-20
- Self Test: TMValues and Wages - 2011-2018
Week 3: Assignment
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2
Readings
-
1
Lab
-
1
Assignment
1
Assignment
- Week 3 Quiz
1
Labs
- Assignment 3 Workspace
2
Readings
- Assignment Overview
- Week 3 - Sample Notebook
Week 3: R Content
-
1
Readings
1
Readings
- Week 3 R Content
Week 4: Lecture Videos
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4
Videos
4
Videos
- Forecasting Model: MLB
- Forecasting Model: NHL Part 1
- Forecasting Model: NHL Part 2
- Forecasting Model: NBA
Week 4: Lectures Notebooks
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3
Lab
3
Labs
- 4.1. NHL Forecasting Model
- 4.2. MLB Forecasting Model
- 4.3. NBA Forecasting Model
Week 4: Assignment
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3
Readings
-
1
Lab
-
1
Assignment
1
Assignment
- Week 4 Quiz
1
Labs
- Assignment 4 Workspace
3
Readings
- Assignment Overview
- Assignment Instructions
- Week 4 - Sample Notebooks
Week 4: R Content
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1
Readings
1
Readings
- Week 4 R Content
Week 5: Lecture Videos
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7
Videos
-
1
Readings
7
Videos
- Gambling and the Development of Probability Theory
- Gambling, Morality, and Sports Part 1
- Gambling, Morality, and Sports Part 2
- Social Policy and Sports Gambling
- Problem Gambling Part 1
- Problem Gambling Part 2
- Match Fixing, Gambling and Sports
1
Readings
- Post-Course Survey
Auto Summary
Unlock the power of big data and analytics with "Prediction Models with Sports Data" on Coursera. This foundational course is designed to teach you how to predict game outcomes in professional sports using Python, with a focus on logistic regression modeling and team expenditure data. Guided by expert instructors, you will journey through the process of modeling past game results and using these models to forecast future ones. The course emphasizes practical applications, including evaluating model reliability with betting odds and analyzing data from the English Premier League, NBA, and NHL. Moreover, you will gain insights into the intricate relationship between data analytics and gambling, exploring historical contexts and the social implications of sports betting. This engaging and comprehensive course spans approximately 34 hours and is available through Coursera's Starter subscription. It is perfect for beginners eager to delve into the world of sports data analytics and predictive modeling. Join now to enhance your analytical skills and transform your passion for sports into data-driven predictions.
Youngho Park
Stefan Szymanski