Soccer Analytics with Machine Learning: Learning Predictive Modeling Techniques with Sports Data

By Weining Shen, Haipeng Gao, Guanyu Hu

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

• Format: Paperback
• Language: English
• ISBN: 1098181115
• ISBN13: 9781098181116
• Release Date: September 2026
• Publisher: O'Reilly Media
• Length: 333 Pages