Preface

"The more I learn about football, the more I realize how much I don't know." — Johan Cruyff

Why This Book Exists

Soccer is the world's most popular sport, watched and played by billions across every continent. In recent decades, a quiet revolution has transformed how the game is understood, analyzed, and played at the highest levels. Data analytics, once viewed with skepticism by traditionalists, has become an indispensable tool for clubs competing for the world's most prestigious trophies.

Yet despite this transformation, a significant gap has persisted between the cutting-edge work being done inside professional clubs and the educational materials available to aspiring analysts. Academic courses often teach general data science skills without soccer-specific context. Industry resources tend to be either too superficial or too specialized. Students eager to enter the field have lacked a comprehensive resource that bridges theory and practice.

This textbook aims to fill that gap.

Who This Book Is For

Professional Soccer Analytics and Visualization is designed primarily for undergraduate students with foundational knowledge in statistics and Python programming. However, the content has been structured to serve multiple audiences:

  • Undergraduate students in sports science, data science, statistics, or computer science programs seeking specialization in soccer analytics
  • Graduate students looking for a comprehensive reference and introduction to the field
  • Self-learners with technical backgrounds who want to transition into sports analytics
  • Current analysts seeking to formalize their knowledge or fill gaps in their education
  • Coaches and technical staff wanting to better understand and leverage analytical insights
  • Journalists and content creators aiming to incorporate advanced statistics into their work

What Makes This Book Different

Several principles guided the development of this textbook:

Theory Meets Practice

Every concept is grounded in both academic rigor and real-world application. We don't just explain what xG is—we build an xG model from scratch, evaluate its performance, discuss its limitations, and show how professional clubs actually use it.

Code First

This is not a book about soccer analytics—it's a book about doing soccer analytics. Every chapter includes substantial Python code that readers can run, modify, and extend. We believe you learn analytics by analyzing, not by reading about analyzing.

Building Complexity

The book is carefully sequenced to build knowledge incrementally. Early chapters establish foundations that later chapters extend. By the time you reach advanced topics like deep learning or pitch control models, you'll have all the prerequisite knowledge in place.

Real Data, Real Problems

Exercises and case studies use realistic data and scenarios drawn from professional contexts. You won't find contrived examples here—the problems you'll solve mirror the problems analysts solve in club offices every day.

Balanced Mathematical Treatment

We take a "medium intensity" approach to mathematics. Every formula is explained intuitively, with emphasis on building understanding rather than merely computing results. Derivations are provided for those who want them but clearly marked for those who don't.

How to Use This Book

For Self-Study

If you're learning independently, we recommend proceeding through the book sequentially, at least for Parts I and II. These foundational chapters establish concepts and vocabulary that later chapters assume.

For each chapter: 1. Read the main content carefully, running all code examples 2. Complete the exercises—learning requires active practice 3. Take the self-assessment quiz and aim for 70% before proceeding 4. Work through at least one case study to solidify understanding

Don't rush. The goal is mastery, not completion.

For Course Instructors

This textbook supports various course structures:

  • Single-semester introduction: Parts I-II (Chapters 1-14)
  • Two-semester sequence: Parts I-III (Chapters 1-21)
  • Advanced course: Parts III-IV with Part I as prerequisite
  • Capstone or project course: Part V with selected earlier material

Each chapter includes more exercises than any single course would use, allowing instructors to select problems matching their emphasis. Solutions to all exercises are available to verified instructors.

For Reference

The appendices provide quick-reference material for working analysts: - Appendix A reviews mathematical foundations - Appendix C provides Python patterns and idioms - Appendix E offers a comprehensive glossary - Appendix D catalogs data sources

Prerequisites

To get the most from this textbook, you should have:

Statistics: - Understanding of descriptive statistics (mean, median, standard deviation) - Familiarity with probability concepts (probability distributions, conditional probability) - Basic exposure to hypothesis testing and confidence intervals - Awareness of regression analysis concepts

Programming: - Comfortable writing Python functions and classes - Experience with basic data structures (lists, dictionaries) - Familiarity with reading documentation and debugging - Some exposure to pandas or similar data manipulation libraries

Soccer: - Understanding of basic soccer rules and gameplay - Familiarity with common tactical concepts (formations, positions) - General awareness of major leagues and competitions

If you're uncertain about your preparation, Chapter 3 (Statistical Foundations) and Chapter 4 (Python Programming) provide comprehensive reviews with soccer-specific examples.

Acknowledgments

A textbook of this scope reflects the contributions of countless individuals across the soccer analytics community. We are indebted to the researchers who have published their methods, the practitioners who have shared their experiences, and the open-source developers who have built the tools we all rely upon.

Special thanks to the StatsBomb team for their commitment to open data, which has democratized soccer analytics education. The mplsoccer and socceraction libraries, along with many others, have made sophisticated analysis accessible to students worldwide.

We are also grateful to the reviewers, instructors, and students who provided feedback on early drafts. Your insights improved every chapter.

A Final Word

Soccer analytics is a young field, still finding its footing between art and science. The best analysts combine rigorous quantitative skills with deep football knowledge and strong communication abilities. No textbook can fully develop all three dimensions—but we hope this one provides a solid foundation in the first while nurturing appreciation for the others.

We wrote this book because we believe in the power of data to illuminate the beautiful game, and because we want to help the next generation of analysts reach even further than we have. If you're holding this book, you're joining a community of curious, passionate people who share that vision.

Welcome. Let's get to work.

The Authors 2024