Part I: Foundations of Soccer Analytics

"Before you can run, you must learn to walk. Before you can analyze, you must understand your tools."

Overview

Part I establishes the essential foundations for your journey into soccer analytics. These six chapters provide the conceptual framework, technical skills, and domain knowledge that all subsequent learning builds upon.

By the end of Part I, you will:

• Understand what soccer analytics is and why it matters
• Know where soccer data comes from and how to access it
• Apply statistical reasoning to soccer questions
• Manipulate and visualize soccer data with Python
• Comprehend the philosophy behind metric design
• Work with spatial data on the soccer pitch

Chapters in This Part

Chapter 1: Introduction to Soccer Analytics

Your entry point into the field. We trace the history of soccer analytics from early pioneers to modern data departments, introduce key stakeholders and career paths, and establish the analytical mindset that will guide your work.

Chapter 2: Data Sources and Collection in Soccer

Data is the raw material of analytics. This chapter catalogs the types of soccer data available—from event data to tracking data to video—explains how it's collected, and teaches you to access both free and commercial sources.

Chapter 3: Statistical Foundations for Soccer Analysis

Soccer analytics is applied statistics. We review essential statistical concepts with soccer-specific examples, covering descriptive statistics, probability, inference, and regression while highlighting common pitfalls in soccer applications.

Chapter 4: Python Programming for Soccer Analytics

Python is the language of modern analytics. This chapter ensures your programming skills are ready for soccer applications, covering data manipulation with pandas, numerical computing with NumPy, and soccer-specific libraries.

Chapter 5: Introduction to Soccer Metrics

Metrics translate observations into numbers. We explore the philosophy of metric design, distinguish good metrics from poor ones, and introduce frameworks for evaluating and communicating analytical measures.

Chapter 6: The Soccer Pitch as a Coordinate System

Soccer is inherently spatial. This chapter teaches you to work with pitch coordinates, create visualizations, and begin thinking about space—a theme that will grow increasingly important throughout the textbook.

Learning Path

These chapters are designed to be completed in sequence:

Chapter 1 ──→ Chapter 2 ──→ Chapter 3
    │             │             │
    ▼             ▼             ▼
  "Why"       "What"      "How (Stats)"

Chapter 4 ──→ Chapter 5 ──→ Chapter 6
    │             │             │
    ▼             ▼             ▼
"How (Code)"  "Metrics"    "Space"

If you have strong backgrounds in certain areas: - Experienced programmers may skim Chapter 4 but should complete its exercises - Statistics students should still read Chapter 3 for soccer-specific applications - Industry professionals should not skip Chapter 1—it provides important context

Time Investment

Plan for approximately 4-6 weeks to complete Part I thoroughly if studying part-time.

What Comes Next

After completing Part I, you'll be ready for Part II: Core Analytics and Metrics. There you'll learn to build xG models, understand expected assists and expected threat, analyze passing networks, measure possession quality, and much more—applying all the foundational skills developed here.

The foundations matter. Invest time here, and the advanced material will follow naturally.

Let's begin with Chapter 1: Introduction to Soccer Analytics.

Chapters in This Part

• Chapter 1: Introduction to Soccer Analytics
• Chapter 2: Data Sources and Collection in Soccer
• Chapter 3: Statistical Foundations for Soccer Analysis
• Chapter 4: Python Tools for Soccer Analytics
• Chapter 5: Introduction to Soccer Metrics
• Chapter 6: The Soccer Pitch as a Coordinate System