Sports analytics and data science : winning the game with methods and models / Thomas W. Miller.

By:

Miller, Thomas W, 1946-

Material type:

TextPublication details: New Jersey : Pearson, c2016.Description: xiii, 337 p. : ill. b&w ; 25 cmISBN:

9780133886436
0133886433

Subject(s):

DDC classification:

796.021

Contents:

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

Summary: 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.

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Holdings
Item type	Current library	Call number	Status	Barcode
Books	Marbella International University Centre Library	796.021 MIL win (Browse shelf(Opens below))	Available	12127

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Previous	792 SAM int V zerkale Brodvei︠a︡ :	796.019 PER spo Sport psychology :	796.019 WEI fou Foundations of sport and exercise psychology /	796.021 MIL win Sports analytics and data science :	796.021 SPO spo Sports analytics :	796.0688 NIC spo Sport and the media :	796.0688 WAT spo Sport teams, fans, and Twitter :	Next

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.

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