Professional Basketball Analytics and Visualization

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31 chapters ~30 hours total 214 pages

1

Front Matter

6 chapters

• Professional Basketball Analytics and Visualization
• Preface
• Acknowledgments
• How to Use This Book
• Prerequisites
• Book Outline

2

Part 1: Foundations of Basketball Analytics

5 chapters

• Chapter 1: Introduction to Basketball Analytics
• Chapter 2: Data Sources and Collection
• Chapter 3: Python Environment Setup
• Chapter 4: Exploratory Data Analysis for Basketball
• Chapter 5: Descriptive Statistics in Basketball

3

Part 2: Traditional Basketball Metrics

5 chapters

• Chapter 6: Box Score Fundamentals
• Chapter 7: Rate Statistics and Pace Adjustment
• Chapter 8: Shooting Efficiency Metrics
• Chapter 9: Advanced Box Score Metrics
• Chapter 10: Plus-Minus and On/Off Analysis

4

Part 3: Modern Analytics

6 chapters

• Chapter 11: Regularized Adjusted Plus-Minus (RAPM)
• Chapter 12: Box Plus-Minus (BPM) and Value Over Replacement Player (VORP)
• Chapter 13: Win Shares and Wins Above Replacement
• Chapter 14: Expected Possession Value (EPV)
• Chapter 15: Player Tracking Analytics
• Chapter 16: Shot Quality Models

5

Part 4: Team and Game Analytics

5 chapters

• Chapter 17: Team Offensive Efficiency
• Chapter 18: Team Defensive Analytics
• Chapter 19: Lineup Optimization
• Chapter 20: Game Strategy and Situational Analysis
• Chapter 21: In-Game Win Probability

6

Part 5: Predictive Modeling

4 chapters

• Chapter 22: Player Performance Prediction
• Chapter 23: Draft Modeling and Prospect Evaluation
• Chapter 24: Injury Risk and Load Management
• Chapter 25: Game Outcome Prediction

7

Part 6: Advanced Topics and Applications

3 chapters

• Chapter 26: Machine Learning in Basketball
• Chapter 27: Computer Vision and Video Analysis
• Chapter 28: Building a Basketball Analytics Career

8

Part 7: Capstone Projects

3 chapters

• Capstone Project 1: Build a Player Comparison Tool
• Capstone Project 2: Create a Draft Model
• Capstone Project 3: Develop a Game Prediction System

9

Appendices

8 chapters

• Appendix A: Mathematical Foundations
• Appendix B: Statistical Tables
• Appendix C: Python Quick Reference
• Appendix D: Data Sources and APIs
• Appendix E: Glossary of Terms
• Appendix F: Notation Guide
• Appendix G: Answers to Selected Exercises
• Appendix H: Bibliography

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