Sports Analytics and Data Science
Winning the Game with Methods and Models
1st Edition
Published on 18. November 2015
352 pages
978-0-13-388741-9 (ISBN)
Description
This is the eBook of the printed book and may not include any media, website access codes, or print supplements that may come packaged with the bound book.
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.
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.
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.
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.
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More details
Series
Edition
1. Auflage
Language
English
Place of publication
NJ
United States
Publishing group
Pearson Education (US)
Target group
College/higher education
File size
18,66 MB
ISBN-13
978-0-13-388741-9 (9780133887419)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Book
01/2016
Pearson FT Press
€73.64
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Person
THOMAS W. MILLER is faculty director of the Predictive Analytics program at Northwestern University. He has designed courses for the program, including Sports Management Analytics, Marketing Analytics, Advanced Modeling Techniques, Data Visualization, Web and Network Data Science, Database Systems and Data Preparation, and the capstone course. He has taught extensively in the program and works with more than 40 other faculty members in delivering training in predictive analytics and data science.
Miller is also owner and president of Research Publishers LLC. He has consulted widely in the areas of retail site selection, product positioning, segmentation, text and sentiment analysis, and pricing in competitive markets, and has worked with predictive models for over 30 years.
Miller's books include Marketing Data Science: Modeling Techniques in Predictive Analytics with R and Python; Web and Network Data Science; Modeling Techniques in Predictive Analytics with Python and R: A Guide to Data Science, and a previous book about predictive modeling in sports, Without a Tout: How to Pick a Winning Team.
Before entering academia, Miller spent nearly 15 years in business IT in the computer and transportation industries. He also directed the A. C. Nielsen Center for Marketing Research and taught market research and business strategy at the University of Wisconsin-Madison.
He holds a Ph.D. in psychology (psychometrics) and a master's degree in statistics from the University of Minnesota, and an MBA and master's degree in economics from the University of Oregon. Find additional content and tools at www.ftpress.com/miller
Miller is also owner and president of Research Publishers LLC. He has consulted widely in the areas of retail site selection, product positioning, segmentation, text and sentiment analysis, and pricing in competitive markets, and has worked with predictive models for over 30 years.
Miller's books include Marketing Data Science: Modeling Techniques in Predictive Analytics with R and Python; Web and Network Data Science; Modeling Techniques in Predictive Analytics with Python and R: A Guide to Data Science, and a previous book about predictive modeling in sports, Without a Tout: How to Pick a Winning Team.
Before entering academia, Miller spent nearly 15 years in business IT in the computer and transportation industries. He also directed the A. C. Nielsen Center for Marketing Research and taught market research and business strategy at the University of Wisconsin-Madison.
He holds a Ph.D. in psychology (psychometrics) and a master's degree in statistics from the University of Minnesota, and an MBA and master's degree in economics from the University of Oregon. Find additional content and tools at www.ftpress.com/miller
Content
1: Understanding Sports Markets
2: Assessing Players
3: Ranking Teams
4: Predicting Scores
5: Making GameDay Decisions
6: Crafting a Message
7: Promoting Brands and Products
8: Growing Revenues
9: Managing Finances
10: Playing Whatif Games
11: Working with Sports Data
12: Competing on Analytics
A: Data Science Methods
A.1: Mathematical Programming
A.2: Classical and Bayesian Statistics
A.3: Regression and Classification
A.4: Data Mining and Machine Learning
A.5: Text and Sentiment Analysis
A.6: Time Series, Sales Forecasting, and Market Response Models
A.7: Social Network Analysis
A.8: Data Visualization
A.9: Data Science: The Eclectic Discipline
B: Professional Leagues and Teams
Data Science Glossary
Baseball Glossary
Bibliography
Index
2: Assessing Players
3: Ranking Teams
4: Predicting Scores
5: Making GameDay Decisions
6: Crafting a Message
7: Promoting Brands and Products
8: Growing Revenues
9: Managing Finances
10: Playing Whatif Games
11: Working with Sports Data
12: Competing on Analytics
A: Data Science Methods
A.1: Mathematical Programming
A.2: Classical and Bayesian Statistics
A.3: Regression and Classification
A.4: Data Mining and Machine Learning
A.5: Text and Sentiment Analysis
A.6: Time Series, Sales Forecasting, and Market Response Models
A.7: Social Network Analysis
A.8: Data Visualization
A.9: Data Science: The Eclectic Discipline
B: Professional Leagues and Teams
Data Science Glossary
Baseball Glossary
Bibliography
Index
