Sports Analytics and Data Science: Winning the Game with Methods and Models

About

Features

Master practical data modeling and analysis for sports: learn to measure and predict both individual and team performance 

• Learn state-of-the-art data science and modeling techniques through cutting edge sports applications
• Packed with examples from sports economics, marketing, management, performance measurement, and competitive analysis
• Teaches methods from economics, accounting, finance, classical and Bayesian statistics, machine learning, simulation, and mathematical programming
• Includes many fully-worked examples in both Python and R
• The fifth book in Thomas W. Miller’s Modeling Techniques in Predictive Analytics series on practical data science: complements Lorena Martin’s new Sports Performance Measurement and Analytics

Description

TO BUILD WINNING TEAMS AND SUCCESSFUL SPORTS BUSINESSES, GUIDE YOUR DECISIONS WITH DATA

Sports Analytics and Data Science is the most accessible and practical guide to sports analytics for everyone who cares about winning and everyone who is interested in data science.

You’ll discover how successful sports analytics blends business and sports savvy, modern information technology, and sophisticated modeling techniques. You’ll master the discipline through realistic sports vignettes and intuitive data visualizations—not complex math.

Thomas W. Miller, leader of Northwestern University’s pioneering program in predictive analytics, guides you through defining problems, identifying data, crafting and optimizing models, writing effective R and Python code, interpreting your results, and more.

Every chapter focuses on one key sports analytics application. Miller guides you through assessing players and teams, predicting scores and making game-day decisions, crafting brands and marketing messages, increasing revenue and profitability, and much more. Step by step, you’ll learn how analysts transform raw data and analytical models into wins: both on the field and in any sports business.

Whether you’re a team executive, coach, fan, fantasy player, or data scientist, this guide will be a powerful source of competitive advantage… in any sport, by any measure.

This exceptionally complete and practical guide to sports data science and modeling teaches through realistic examples from sports industry economics, marketing, management, performance measurement, and competitive analysis.

Thomas W. Miller, faculty director of Northwestern University’s pioneering Predictive Analytics program, shows how to use advanced measures of individual and team performance to judge the competitive position of both individual athletes and teams, and to make more accurate predictions about their future performance.

Miller’s modeling techniques draw on methods from economics, accounting, finance, classical and Bayesian statistics, machine learning, simulation, and mathematical programming. Miller illustrates them through realistic case studies, with fully worked examples in both R and Python.

Sports Analytics and Data Science will be an invaluable resource for everyone who wants to seriously investigate and more accurately predict player, team, and sports business performance, including students, teachers, sports analysts, sports fans, trainers, coaches, and team and sports business managers. It will also be valuable to all students of analytics and data science who want to build their skills through familiar and accessible sports applications

• Understanding sports markets
• Assessing players
• Rank
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Downloads

Downloads

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Extras

Author's Site

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Sample Content

Sample Pages

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Table of Contents

• 1: Understanding Sports Markets   
• 2: Assessing Players   
• 3: Ranking Teams   
• 4: Predicting Scores   
• 5: Making GameDay Decisions   
• 6: Crafting a Message   
• 7: Promoting Brands and Products   
• 8: Growing Revenues   
• 9: Managing Finances   
• 10: Playing Whatif Games   
• 11: Working with Sports Data   
• 12: Competing on Analytics   
• A: Data Science Methods   
• A.1: Mathematical Programming   
• A.2: Classical and Bayesian Statistics   
• A.3: Regression and Classification   
• A.4: Data Mining and Machine Learning   
• A.5: Text and Sentiment Analysis   
• A.6: Time Series, Sales Forecasting, and Market Response Models   
• A.7: Social Network Analysis   
• A.8: Data Visualization   
• A.9: Data Science: The Eclectic Discipline   
• B: Professional Leagues and Teams   
• Data Science Glossary   
• Baseball Glossary   
• Bibliography   
• Index   

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