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

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

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  • Copyright 2016
  • Dimensions: 7" x 9-1/4"
  • Edition: 1st
  • eBook (Watermarked)
  • ISBN-10: 0-13-388739-1
  • ISBN-13: 978-0-13-388739-6


This up-to-the-minute reference will help you master all three facets of sports analytics – and use it to win!

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.

All data sets, extensive R and Python code, and additional examples available for download at http://www.ftpress.com/miller/

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

Gain powerful, actionable insights for:

  • Understanding sports markets
  • Assessing players
  • Ranking teams
  • Predicting scores
  • Making game day decisions
  • Crafting marketing messages
  • Promoting brands and products
  • Growing revenues
  • Managing finances
  • Playing what-if games
  • And much more



Download individual code files by chapter.

Sample Content

Table of Contents

Preface    v

Figures    ix

Tables    xi

Exhibits    xiii

1: Understanding Sports Markets    1

2: Assessing Players    23

3: Ranking Teams    37

4: Predicting Scores    49

5: Making GameDay Decisions    61

6: Crafting a Message    69

7: Promoting Brands and Products    101

8: Growing Revenues    119

9: Managing Finances    133

10: Playing Whatif Games    147

11: Working with Sports Data    169

12: Competing on Analytics    193

A: Data Science Methods    197

A.1: Mathematical Programming    200

A.2: Classical and Bayesian Statistics    203

A.3: Regression and Classification    206

A.4: Data Mining and Machine Learning    215

A.5: Text and Sentiment Analysis    217

A.6: Time Series, Sales Forecasting, and Market Response Models    226

A.7: Social Network Analysis    230

A.8: Data Visualization    234

A.9: Data Science: The Eclectic Discipline    240

B: Professional Leagues and Teams    255

Data Science Glossary    261

Baseball Glossary    279

Bibliography    299

Index    329


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