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

1. Understanding sports markets --
2. Assessing players --
3. Ranking teams --
4. Predicting scores --
5. Making game-day decisions --
6. Crafting a message --
7. Promoting brands and products --
8. Growing revenues --
9. Managing finances --
10. Playing what-if games --
11. Working with sports data --
12. Competing on analytics --
A. Data Science Methods --
B. Professional Leagues and Teams

This is a complete, practical guide to sports data science and modeling, with 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 Python and R.