Sports Analytics and Data Science: Winning the Game with Methods and Models (FT Press Analytics), by Thomas W. Miller
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Sports Analytics and Data Science: Winning the Game with Methods and Models (FT Press Analytics), by Thomas W. Miller
Best Ebook Sports Analytics and Data Science: Winning the Game with Methods and Models (FT Press Analytics), by Thomas W. Miller
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.
Sports Analytics and Data Science: Winning the Game with Methods and Models (FT Press Analytics), by Thomas W. Miller
- Amazon Sales Rank: #305125 in eBooks
- Published on: 2015-11-18
- Released on: 2015-11-18
- Format: Kindle eBook
About the Author
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
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Most helpful customer reviews
2 of 3 people found the following review helpful. Entry Level College textbook - no real insight into sports analytics - expensive By Graham Webster I should have realized what I was buying by looking at the name of the publisher: Pearson Education. This books reads as a very basic introduction to sports analytics and sports marketing / sport as business - as a text for an entry level College course. There is no real insight into sports analytics, and at $US68 for the Kindle edition, this book is clearly very poor value for money.Specific criticisms are:- Aims to provide wide coverage, but ends up providing no depth in it's coverage.- Far too many references in the text. The author provides a range of ideas that could be applied to sports analytics, but does not develop them. Instead, a lit of references is provided for the reader to investigate. For example, social network analysis is introduced, and then the reader is refered to a number of references: "small world networks have been studied extensively in social psychology, sociology and network science (Milgram 1967, Travers and Milgram 1969, Watts 1999; Schnettler 2009) " . Just the introduction of an idea, no real development of it's use in sports analytics and a reading list. If the author wanted to retain the references in the next edition, I'd advise him to have an annotated bibliography.- A lot of unnecessary material. For example, table 1.1 is a list of 54 different sports. Using the % of book feature in the Kindle edition, 59% of the book is taken up with a glossary, baseball glossary, bibliography and index. The extent of the material is excessive; for example, the glossary defines such terms as "e-mail", "html", "internet", "IT" and "World Wide Web". For a book on sports analytics, I don't see any point in including these and other terms in a glossary.- There are a number of R and Python scripts, which interestingly are described as Exhibits. The author doesn't give any practical guidance on analysis of sports data; these R (and Python) scripts are as good as exhibits in a museum.There are many far superior books available on sports analytics, and I'd recommend these instead of this book.Some suggestions;- Analyzing Wimbleton - The Power of Statistics- Basketball Analytics - Spatial Tracking- Baseball Analytics - Objective and Efficient Strategies for Understanding How Teams Win- Analyzing Baseball Data With R- Mathletics
2 of 4 people found the following review helpful. I am reticent to give this tome a perfect score because I have only read three chapters By Chris Rucker I am reticent to give this tome a perfect score because I have only read three chapters; however, I will say that sufficient explanations are given alongside workable code. To that end, I would offer a buy recommendation based on examples in both R and Python.One potential caveat is that the links appear to point to nowhere in particular in terms of data sets and programs. But, I am confident this information is available somewhere, or will be.
5 of 9 people found the following review helpful. Selling a book that offers code for the examples but not having the code accessible is poor form. By anonymous I am 6 chapters in and the reason I am giving this book two stars is that the code for the chapter examples is still no where to be found. The book indicates it is on the publishers website - [...] it is not there and I have attempted to contact the publisher twice but they have not gotten back to me either. This is a huge swing and a miss (no pun intended) by the author and the publisher. Selling a book that offers code for the examples but not having the code accessible is poor form.
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