Getting Started

Quick Start Guide

A structured path to mastering sports pricing models. Follow this roadmap from foundations to advanced topics.

📋 Prerequisites

Basic Probability

Essential

Probability distributions, expected value, variance

Statistics

Essential

Hypothesis testing, confidence intervals, regression

Calculus

Helpful

Optimization, derivatives (for understanding derivations)

Python/R/JS

Essential

Any one language for running simulations

Sports Knowledge

Helpful

Understanding of sports betting markets

Learning Path

🏗️

Phase 1: Foundations

~2 hours

Expected Value

The fundamental building block

Implied Probability

Converting odds to probabilities

Regression Models

Predicting player outcomes

📊

Phase 2: Core Statistical Models

~4 hours

Bayesian Updating

Real-time line adjustments

Monte Carlo Simulation

Modeling uncertainty

Kelly Criterion

Optimal bet sizing

⚖️

Phase 3: Risk & Pricing

~4 hours

Correlation Analysis

Managing correlated picks

Value at Risk

Worst-case scenarios

Market Making

Balancing the book

🚀

Phase 4: Advanced Topics

~6 hours

Ensemble Methods

Combining multiple models

Reinforcement Learning

Adaptive pricing

Game Theory

Strategic interactions

💡 Learning Tips

✓ Use the interactive widgets - don't just read, experiment!
✓ Run the R/Python code examples in your own environment
✓ Try the Playground to combine multiple concepts
✓ Take notes using the built-in note system
✓ Focus on intuition first, then formulas

🎯 Key Connections

→ EV + Kelly: EV tells you IF to bet, Kelly tells you HOW MUCH
→ Bayesian + Monte Carlo: Update beliefs then simulate outcomes
→ Correlation + VaR: Correlated bets amplify risk
→ Regression + Ensemble: Combine models for better predictions

Start with Expected Value →