Soccer analytics with machine learning : learning predictive modeling techniques with sports data / Haipeng Gao, Ari Joury, Guanyu Hu & Weining Shen.

Format:

Book

Author/Creator:

Gao, Haipeng, author.

Joury, Ari, author.

Hu, Guanyu, author.

Shen, Weining, author.

Language:

English

Subjects (All):

Sports--Statistical methods.

Sports.

Artificial intelligence--Statistical methods.

Artificial intelligence.

Sports--Technological innovations.

Machine learning.

Physical Description:

1 online resource.

Place of Publication:

Sebastopol, CA : O'Reilly Media, [2026]

Summary:

Struggling to grasp machine learning concepts or unsure how to apply them in the real world? This book aims to change that by using the world's most popular game--soccer--to illuminate key concepts in predictive modeling and data science. Whether you're a complete beginner or you're interested in entering the burgeoning field of sports analytics, you'll develop a solid foundation in machine learning through engaging examples that bridge academic principles with practical applications. Written by experts in both machine learning and sports analytics, this practical Python-focused guide introduces fundamental data science techniques using real soccer data. Ideal for students, analysts, and soccer fans alike, it offers instructions on models and techniques such as logistic regression, random forests, deep learning, simulations, and feature engineering. But instead of memorizing algorithms, you'll learn by building predictive models to analyze match outcomes, test betting strategies, run simulated game scenarios, and more. Understand machine learning concepts by working with real sports data Develop, refine, and evaluate machine learning models, using Python for data analysis Carry out detailed analyses and research on soccer game predictions and betting strategies to surface valuable insights Apply the skills you learn to predictive modeling scenarios in other industries.

Contents:

Cover

Copyright

Table of Contents

Preface

Who Should Read This Book

Why We Wrote This Book

Navigating This Book

Conventions Used in This Book

Using Code Examples

O'Reilly Online Learning

How to Contact Us

Acknowledgments

Chapter 1. The Soccer Analytics Landscape

The Problems That Soccer Analytics Helps Solve

Soccer Clubs: Making Decisions Across the Organization

Betting Markets: Modeling Uncertainty and Pricing Outcomes

Fans and Analysts: Reading the Game Beyond the Scoreline

The Data-Driven Evolution in Soccer

From Outcomes to Process: The Limits of Traditional Analysis

The Data Revolution: From Event Logs to Tracking Systems

Analytics in Practice: Metrics, Applications, and Scope

Why Soccer Resists Easy Analysis

A Framework for Soccer Analysis

The Three Types of Analysis

Data Sources for Modern Analysis

From Player to League: Levels of Aggregation

Principles That Run Through Everything

Looking Ahead: A Guided Tour of the Book

Conclusion

Chapter 2. Python Fundamentals: Building Your Analytics Toolkit

Python: The Language Behind Modern Analytics

Your Analytics Workspace: Setting Up the Environment

Installation: Getting Python Ready

Virtual Environments: Isolating Your Projects

Jupyter Notebook: Creating a Workspace for Interactive Analysis and More

Repositories: Storing Your Project Files

Python Basics: The Core Moves

Primitive Data Types: Representing Basic Information

Basic Operations: Working with Values and Comparisons

Variable Assignment: Storing and Reusing Information

Data Structures: Organizing Information

Conditional Statements: Writing Decision Rules

Loops: Repeating Computation Efficiently

List Comprehensions: Performing Compact and Pythonic Iteration

Functions: Providing Reusable Logic for Analysis

Looking Ahead: From Python Basics to Real Analysis

Chapter 3. Exploratory Data Analysis in Soccer

Understanding Soccer Data Levels

Match-Level Data

Player-Level Data

Event-Level Data

What Is EDA in Soccer Analytics?

Loading StatsBomb Soccer Data

Getting the Data

Loading the Data

Taking a First Look at the Data

Structured EDA by Data Types

Numerical Features: The Quantifiable Game

Categorical Features: The Qualitative Aspects

Ordinal Features: The Ordered Categories

Preliminary Exploration: Getting Your Bearings

Building Tournament DataFrames

Counting the Frequency of Event Types

Filtering: Passes by Player or Team

Grouping: Per-Match Pass Summaries

Aggregating: Shots, Goals, and xG by Team

Basic Data Cleaning for Soccer

Handling Missing Values

Standardizing Names

Visualizing Soccer Data

A Quick Note on Matplotlib and Seaborn

Bar Charts and Rankings

Histograms and Distributions

Boxplots and Variability

Time Series and Cumulative Plots

Notes:

OCLC-licensed vendor bibliographic record.

OCLC:

1573515460