From the Book
Preface
A note to the reader
Dear reader,
Soccer has always been a game driven by passion, intuition, and endless debate. For decades, fans have loved it not only for its results, but for the stories, emotions, memories, and uncertainty that come with every match.
In recent years, that traditional way of enjoying the game has been joined by something new: data. Advances in artificial intelligence, machine learning, modern computing, and the growing availability of high-quality sports data have made rigorous analysis accessible far beyond professional clubs and research labs.
What excites us most is not only the technology itself, but what it enables: clearer thinking, more disciplined reasoning, and a deeper appreciation of soccer's complexity. Data-driven methods do not replace passion or intuition. They sharpen them.
We wrote this book for soccer fans, students, educators, and professionals who want to learn machine learning through real, meaningful problems. It is meant to be practical, honest about limitations, and welcoming to readers who are curious enough to work with code and data.
Its publication in 2026, just ahead of the FIFA World Cup, feels especially meaningful. At a moment when global attention turns toward matches, tactics, and prediction, we hope this book offers a way to engage with the game more deeply.
With curiosity and appreciation for the game,
The Authors
What you'll gain from this book
Learn how to turn soccer data into insights, predictions, and practical machine learning workflows.
Python Foundations
Build the programming and data analysis skills needed to work confidently with soccer analytics projects.
Chapter 2
Exploratory Analysis
Understand match, event, and player data through visualization, summary statistics, and soccer-specific examples.
Chapter 3
Predictive Modeling
Learn classification and regression techniques for match outcomes, scoring, and performance analysis.
Chapters 4-6
Deep Learning
Move from intuition to implementation with neural networks and modern modeling workflows for soccer problems.
Chapter 7
Feature Engineering
Discover how domain knowledge and thoughtful data representation can unlock stronger model performance.
Chapter 8
Decision Making
Connect predictions to betting, optimization, evaluation, and the future direction of soccer analytics.
Chapters 9-10Chapters
What the book covers
-
Chapter 01
The Soccer Analytics Landscape
An introduction to the field, the data ecosystem, and why soccer is such a compelling domain for analytics.
-
Chapter 02
Python Fundamentals
Core Python skills for analysis, data wrangling, and reproducible workflows.
-
Chapter 03
Exploratory Data Analysis in Soccer
Practical tools for understanding event data, team behavior, and player tendencies.
-
Chapter 04
Predicting Soccer Outcomes
Classification methods for forecasting match outcomes and related soccer events.
-
Chapter 05
Advanced Classification Methods
More powerful models and evaluation strategies for applied soccer prediction tasks.
-
Chapter 06
Regression Techniques
Methods for modeling goals, performance metrics, and continuous outcomes in soccer analytics.
-
Chapter 07
Deep Learning for Soccer Analytics
Neural network concepts, implementation ideas, and practical challenges in real applications.
-
Chapter 08
Feature Engineering for Soccer Analytics
How to create better signals from raw soccer data using context and domain expertise.
-
Chapter 09
From Predictions to Potential Profit
A bridge from predictive models to evaluation, optimization, and responsible betting strategy.
Get The Book
Where to read and explore more
-
Retail
Buy-
Choose from major retailers in one place
-
Print availability and easy direct purchase
-
Good for sharing with readers, teams, and classes
-
-
Online
Read-
Read through O'Reilly Learning
-
Ideal for online access
-
Useful for teams and subscribers
-
Direct publisher platform
-
-
Resources
Code-
Official Jupyter notebooks
-
Supplementary materials
-
Chapter-by-chapter examples
-
Setup instructions included
-
Why This Book
What makes it worth reading
-
Practical
Built around real soccer analytics problems instead of abstract machine learning exercises.
-
Comprehensive
Covers Python, exploratory analysis, classification, regression, deep learning, and feature engineering.
-
Applied
Connects predictive models to decisions, evaluation, optimization, and real-world reasoning.
-
Accessible
Welcomes readers with basic programming familiarity, even without prior sports analytics experience.
-
Supported
Includes companion notebooks and supplementary materials so readers can follow along in code.
-
Timely
Arrives as the 2026 tournament cycle brings fresh attention to soccer data, forecasting, and performance analysis.
Newsletter