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: