Sports analytics and data science : winning the game with methods and models

著者

書誌事項

Sports analytics and data science : winning the game with methods and models

Thomas W. Miller

Pearson Education, c2016

  • : [hardback]

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注記

"First printing November 2015"--T.p. verso

Includes bibliographical references (p. 299-328) and index

内容説明・目次

内容説明

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

目次

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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