How to Use This Book

Book Structure

This textbook is organized into seven parts, progressing from foundational concepts to advanced applications:

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* - Part
  - Title
  - Focus
* - 1
  - Foundations
  - Data sources, Python setup, exploratory analysis
* - 2
  - Traditional Metrics
  - Box scores, shooting efficiency, plus-minus
* - 3
  - Modern Analytics
  - RAPM, BPM, Win Shares, player tracking
* - 4
  - Team and Game
  - Offensive/defensive efficiency, lineup optimization
* - 5
  - Predictive Modeling
  - Player projections, draft models, game prediction
* - 6
  - Advanced Topics
  - Machine learning, computer vision, career development
* - 7
  - Capstone Projects
  - Comprehensive portfolio projects

Chapter Components

Each chapter follows a consistent structure to support learning:

Learning Objectives

Every chapter opens with clear, measurable objectives stating what you will be able to do after completing the chapter.

Main Content

The core material includes: - Conceptual explanations with real-world context - Mathematical formulas with step-by-step derivations - Python code that implements each concept - Visualizations that illustrate key ideas - Examples using actual NBA data

Exercises

Each chapter includes 25-40 exercises across difficulty levels: - Basic (★): Reinforce fundamental concepts - Intermediate (★★): Apply concepts to new situations - Advanced (★★★): Extend methods or combine multiple concepts - Challenge (★★★★): Open-ended research questions

Quiz Questions

Self-assessment quizzes with 20-30 questions help you verify your understanding before moving on.

Case Studies

Two case studies per chapter provide extended, real-world applications that go beyond simple examples.

Key Takeaways

A concise summary of the most important points from each chapter.

Further Reading

An annotated bibliography points to academic papers, blog posts, and other resources for deeper exploration.

Suggested Learning Paths

Path 1: Complete Course (Full Semester)

Work through all chapters in order. This path is appropriate for: - University courses in sports analytics - Self-learners seeking comprehensive knowledge - Career changers preparing for analytics roles

Suggested pace: 1-2 chapters per week

Path 2: Essentials (Half Semester)

Focus on core chapters for a faster introduction: - Chapters 1-5: Foundations - Chapters 6, 8, 10: Key traditional metrics - Chapters 11, 12, 13: Modern player metrics - Chapter 22: Player prediction - One capstone project

Suggested pace: 2 chapters per week

Path 3: Advanced Analytics (For Those with Background)

If you already have Python and statistics experience: - Skim Chapters 1-5 (review as needed) - Focus on Parts 3-5 - Complete Parts 6-7

Path 4: Career Preparation

For those seeking jobs in basketball analytics: - Chapter 28: Career development (read first) - Complete Paths 1 or 2 - All three capstone projects - Build portfolio from exercises and projects

Technical Setup

Required Software

  • Python 3.9 or higher
  • Jupyter Notebook or JupyterLab
  • Git (for version control)

Installation

# Create virtual environment
python -m venv basketball-analytics
source basketball-analytics/bin/activate  # Windows: basketball-analytics\Scripts\activate

# Install requirements
pip install -r requirements.txt

Running Code Examples

All code examples are available in two formats: 1. In-chapter code blocks: Copy and run in your own notebooks 2. Chapter code folders: Complete scripts in ch##/code/ directories

Tips for Success

Active Learning

  • Run all code examples yourself, don't just read them
  • Modify examples to explore how changes affect results
  • Complete exercises before checking answers
  • Attempt case studies before reading solutions

Building Understanding

  • Work sequentially through chapters—later concepts build on earlier ones
  • Review prerequisites if you struggle with a chapter
  • Use the glossary for unfamiliar terms
  • Consult further reading for deeper understanding

Portfolio Development

  • Save your best work from exercises and case studies
  • Document your process, not just results
  • Create visualizations that tell a story
  • Complete capstone projects as portfolio pieces

Getting Help

  • Review relevant sections when stuck on exercises
  • Check the appendices for reference material
  • Search online resources for additional explanations
  • Connect with the community through forums and social media

For Instructors

Course Adoption

This textbook is designed for semester-long courses in: - Sports Analytics - Applied Data Science - Sports Management (quantitative track) - Business Analytics (sports focus)

Supplementary Materials

Available to adopting instructors: - Lecture slides - Exam question banks - Additional datasets - Project rubrics - Solution manuals

Customization

The modular structure allows instructors to: - Adjust chapter coverage based on course length - Supplement with additional readings - Modify exercises for local context - Create custom learning paths

Conventions Used in This Book

Code Formatting

# Python code appears in blocks like this
import pandas as pd
df = pd.read_csv('nba_data.csv')

Mathematical Notation

Formulas use LaTeX notation:

$$ \text{TS\%} = \frac{\text{PTS}}{2 \times (\text{FGA} + 0.44 \times \text{FTA})} $$

Admonitions

Notes provide additional context or clarification.

Tips offer practical advice for implementation.

Warnings highlight common mistakes or pitfalls.

Important boxes emphasize critical concepts.

Ready to begin? Start with Chapter 1: Introduction to Basketball Analytics.