Sports Analytics in Python
About this book
Written by MIT AI PhD and business school professor Dr. Michael de la Maza, this is not another dry, academic textbook. You can tell that by the funny looking cover! This is a hands-on playbook for translating your passion for sports into a powerful analytical edge. Forget surface-level stats. You’re about to go behind the curtain and build the same kinds of models used by professionals.Inside, you won't just read about analytics—you'll build it from scratch. You Recreate the Moneyball Build the regression models that allowed the Oakland A's to outsmart the league and quantify exactly how many runs lead to a win.Master the NBA Move beyond basic shooting percentages. Build models to calculate expected Field Goal Percentage (eFG%), visualize player shot charts, and identify the league's most underrated scorers.Solve Football's Biggest Use probabilistic models to definitively answer the should they go for it on 4th and 1? You'll build the tool that makes the right call when the game is on the line.Decode the Beautiful Use network science to create and visualize soccer passing networks, revealing team tactics, key playmakers, and the hidden geometry of an attack.Dominate the Apply machine learning to cricket data to predict player performance and uncover the metrics that truly matter in the modern game.Those are just some of the chapters from the book. Whether you are interested in soccer, golf, or esports, there is a chapter for you.Throughout the book you will learn how to use everything from simple statistics like linear regression to advanced machine learning models like decision trees and neural networks to analyze sports data.Whether you want to dominate your fantasy league, start a career in sports analytics, or just be the smartest person in the room during the game, this book is your ticket.