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

Gao, Haipeng; Joury, Ari; Shen, Weining; Hu, Guanyu

Softcover

ISBN 10: 1098181115 ISBN 13: 9781098181116

Verlag: O'Reilly Media, 2026

Inhaltsangabe

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

Über diesen Titel

Über die Autorin bzw. den Autor

Haipeng Gao is a data science and machine learning expert with extensive industry experience building and optimizing large-scale machine learning systems at leading technology companies, including LinkedIn, TikTok, and PayPal. He holds a PhD in Statistics and Operations Research from the University of North Carolina at Chapel Hill, has taught statistics and probability at UNC Chapel Hill and San Jose State University, and holds multiple AI/ML patents granted by the U.S. Patent and Trademark Office.

Details zur Ausgabe

• Verlag: O'Reilly Media
• Erscheinungsdatum: 2026
• Sprache: Englisch
• ISBN 10: 1098181115
• ISBN 13: 9781098181116
• Einband: Taschenbuch
• Anzahl der Seiten: 300
• Kontakt zum Hersteller: Nicht verfügbar
• Verantwortliche Person: gpsr@libri.de; gpsr@libri.de; ; Friedensallee 273; Hamburg; 22763; Deutschland