Book Outline

Professional Basketball Analytics and Visualization

A Data-Driven Approach to NBA Basketball

Part 1: Foundations of Basketball Analytics

Chapter 1: Introduction to Basketball Analytics

History of basketball statistics
The Moneyball effect and the analytics revolution
Key figures in basketball analytics
How analytics has changed the modern NBA
Overview of this textbook

Chapter 2: Data Sources and Collection

NBA API and official statistics
Basketball Reference and web scraping
Play-by-play data structure
Player tracking data (Second Spectrum)
Public datasets and Kaggle resources
Data quality and limitations

Chapter 3: Python Environment Setup

Installing Python and package managers
Essential libraries for basketball analytics
Jupyter notebooks and development workflow
Version control with Git
Best practices for reproducible analysis

Chapter 4: Exploratory Data Analysis for Basketball

Loading and inspecting NBA data
Data cleaning and preprocessing
Handling missing values
Visualizing distributions and relationships
Time series analysis of player/team performance

Chapter 5: Descriptive Statistics in Basketball

Measures of central tendency
Variability and spread
Percentiles and rankings
Correlation analysis
Summary statistics for basketball data

Part 2: Traditional Basketball Metrics

Chapter 6: Box Score Fundamentals

Points, rebounds, assists, steals, blocks
Turnovers and personal fouls
Minutes played and usage patterns
Historical context of box score statistics
Limitations of counting statistics

Chapter 7: Rate Statistics and Pace Adjustment

Per-minute statistics
Per-possession statistics
Pace calculation and league trends
Pace-adjusted comparisons
Era adjustments

Chapter 8: Shooting Efficiency Metrics

Field goal percentage
Effective field goal percentage (eFG%)
True shooting percentage (TS%)
Shot distribution analysis
The three-point revolution

Chapter 9: Advanced Box Score Metrics

Player Efficiency Rating (PER)
Game Score
Player Impact Estimate (PIE)
Usage rate and assist rate
Rebounding percentages

Chapter 10: Plus-Minus and On/Off Analysis

Raw plus-minus
On/off court splits
Lineup analysis basics
Net rating
Limitations of raw plus-minus

Part 3: Modern Analytics

Chapter 11: Regularized Adjusted Plus-Minus (RAPM)

The lineup problem
Ridge regression fundamentals
Building a RAPM model
Interpreting RAPM values
Multi-year RAPM and priors

Chapter 12: Box Plus-Minus (BPM) and VORP

BPM methodology
Offensive and defensive BPM
Value Over Replacement Player
Comparing BPM to RAPM
Applications and limitations

Chapter 13: Win Shares and Wins Above Replacement

Win Shares calculation
Offensive and defensive win shares
Wins Above Replacement (WAR)
Player valuation and contracts
Historical comparisons

Chapter 14: Expected Possession Value (EPV)

Real-time game valuation
Spatial analysis of court position
Action valuation
Decision-making assessment
Applications in coaching

Chapter 15: Player Tracking Analytics

Second Spectrum technology
Speed and distance metrics
Spatial analysis and positioning
Movement patterns
Defensive tracking metrics

Chapter 16: Shot Quality Models

Expected points models
Shot difficulty factors
Defender proximity
Shot creation value
Shooting luck vs. skill

Part 4: Team and Game Analytics

Chapter 17: Team Offensive Efficiency

Offensive rating calculation
Play type analysis
Spacing and floor balance
Ball movement metrics
Half-court vs. transition offense

Chapter 18: Team Defensive Analytics

Defensive rating
Rim protection
Perimeter defense
Defensive versatility
Opponent shot quality

Chapter 19: Lineup Optimization

Lineup net rating
Two-man and three-man combinations
Rotation analysis
Lineup construction principles
Stagger and closing lineups

Chapter 20: Game Strategy and Situational Analysis

Clutch performance
End-of-game decisions
Timeout usage
Fouling strategy
Pace manipulation

Chapter 21: In-Game Win Probability

Building win probability models
Feature engineering for games
Model calibration
Real-time applications
Win probability added (WPA)

Part 5: Predictive Modeling

Chapter 22: Player Performance Prediction

Regression models for projections
Aging curves
Similarity scores
CARMELO and RAPTOR systems
Projection uncertainty

Chapter 23: Draft Modeling and Prospect Evaluation

College statistics translation
Physical measurements
Combine performance
Draft position value
Bust probability

Chapter 24: Injury Risk and Load Management

Injury data and patterns
Workload metrics
Risk factor identification
Prevention strategies
Rest optimization

Chapter 25: Game Outcome Prediction

Point spread modeling
Over/under prediction
Betting market efficiency
Feature engineering for games
Model evaluation

Part 6: Advanced Topics and Applications

Chapter 26: Machine Learning in Basketball

Clustering player types
Classification problems
Neural networks for basketball
Ensemble methods
Feature importance and interpretability

Chapter 27: Computer Vision and Video Analysis

Tracking technology
Pose estimation
Action recognition
Automated play classification
Future directions

Chapter 28: Building a Basketball Analytics Career

Industry overview
Required skills
Portfolio building
Interview preparation
Career paths

Part 7: Capstone Projects

Capstone 1: Build a Player Comparison Tool

Requirements and design
Data integration
Similarity algorithms
Visualization dashboard
Deployment

Capstone 2: Create a Draft Model

Data collection and preparation
Feature engineering
Model development
Evaluation and backtesting
Presentation

Capstone 3: Develop a Game Prediction System

System architecture
Real-time data integration
Prediction algorithms
Performance tracking
Continuous improvement

Appendices

Appendix A: Mathematical Foundations

Linear algebra basics
Probability theory
Statistical inference
Regression analysis

Appendix B: Statistical Tables

Critical values
Probability distributions
Reference tables

Appendix C: Python Reference

Common functions
Library quick reference
Code snippets

Appendix D: Data Sources

NBA API documentation
Basketball Reference guide
Additional resources

Appendix E: Glossary

Terms and definitions
Acronyms

Appendix F: Notation Guide

Mathematical notation
Statistical symbols

Appendix G: Answers to Selected Exercises

Chapter solutions
Code answers

Appendix H: Bibliography

References
Further reading