Interested In Learning Quantitative Sports Betting For Yourself?

We are currently building a course on Udemy that will provide a comprehensive introduction to algorithmic sports betting. It will be comprised of two sections - learning how to build a model that can consistently make accurate predictions, and then learning how and when to apply them to make bets. A tentative outline of course material is provided below:

Section 1: Building a Sports Betting Model

• Data Collection
• Data Cleaning and Dataset Creation
• Where to Build?
• Dataset Partioning and Why It's Important
• Exploratory Analysis & Classification/Regression Decision
• Which Type of Model?
• Feature Selection
• Hyperparameter Tuning
• Model Fitting & Testing
• Model Calibration

Section 2: Using Your Model to Make Bets

• Intro to +EV Wagering
• Intro to the Kelly Criterion
• Exploring the Potential Parameters of Your Betting System
• Validation to Determine Betting Thresholds
• Common Errors to Avoid When Setting Betting Thresholds
• Final Out-of-Sample Model Testing
• Performance Analysis of Your Out-of-Sample Testing
• Implementing Model for Live Release
• Live Performance Tracking & Parsing Signal vs. Noise in Your Results