- Series: Chapman & Hall/CRC The R Series (Book 14)
- Paperback: 352 pages
- Publisher: Chapman and Hall/CRC; 1 edition (October 29, 2013)
- Language: English
- ISBN-10: 1466570229
- ISBN-13: 978-1466570221
- Product Dimensions: 1 x 6 x 9 inches
- Shipping Weight: 1 pounds (View shipping rates and policies)
- Average Customer Review: 4.4 out of 5 stars See all reviews (28 customer reviews)
- Amazon Best Sellers Rank: #49,535 in Books (See Top 100 in Books)
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Analyzing Baseball Data with R (Chapman & Hall/CRC The R Series) 1st Edition
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"There are some great resources out there for learning R and for learning how to analyze baseball data with it. In fact, a few pretty smart people wrote a fantastic book on the subject, coincidentally titled Analyzing Baseball Data with R. I can’t say enough about this book as a reference, both for baseball analysis and for R. Go and buy it."
―Bill Petti, The Hardball Times, September 2015
"The authors present a potpourri of well-conceived case-studies that give insight into both the game’s complexity and R’s simplicity. Virtually no previous knowledge of statistical theory and software is required to master the data analyses and to follow the explications in this book … The authors’ style of writing is pleasurable and bespeaks their passion for the game. Narratives and R commands are so smoothly intermingled that the source code hardly disturbs the flow of reading, and a wealth of graphs break up the grey. … A great asset of the book is that it encourages the reader to learn the ropes of sabermetrics by actually running the example analyses on one’s own computer."
―Journal of the Royal Statistical Society, Series A, 2015
"If you are interested in statistics, especially baseball statistics, you will find this book fascinating and very useful. It provides many details. websites, and useful descriptions for using the R programming environment. This is not only a book on statistics; there are many references to famous player statistics, making this a very enjoyable book to read. And even if you don’t like baseball but still find statistics very exciting, then this book provides a great introduction to R that can be used for any other type of statistical data set."
―IEEE Insulation Magazine, November/December 2014
"I have spent most of the past decade working in baseball as a statistical analyst for the New York Mets. … This type of employment can be highly valued, especially among quantitatively inclined college students who are coincidentally passionate baseball fans. It is from these students from whom I am most frequently asked, ‘what book would you recommend for someone who wants to get started in sabermetrics?’ Invariably, my response has been [Jim Albert and Jay Bennett’s] Curve Ball. I have a new response. …
I always felt that Curve Ball was the best place for a budding sabermetrician to start … However, it later dawned on me that while Curve Ball provided a sound framework for thinking probabilistically about baseball, I devoted a huge proportion of my time at work to computer programming. …
In their new book, Albert and Max Marchi, a native Italian who now works for the Cleveland Indians, have closed the loop by offering the aspiring sabermetrician a blueprint. … The reader who digests this book alongside her keyboard will emerge as a practicing sabermetrician―having knowledge of the key ideas in sabermetric theory, a historical understanding of from whence those ideas came, and the practical ability to compute with baseball data. It is a sabermetric workshop in paperback."
―Ben S. Baumer, International Statistical Review (2014), 82
About the Author
Max Marchi is a baseball analyst with the Cleveland Indians. He was previously a statistician at the Emilia-Romagna Regional Health Agency. He has been a regular contributor to The Hardball Times and Baseball Prospectus websites and has consulted for MLB clubs.
Jim Albert is a professor of statistics at Bowling Green State University. He has authored or coauthored several books and is the editor of the Journal of Quantitative Analysis of Sports. His interests include Bayesian modeling, statistics education, and the application of statistical thinking in sports.
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Top Customer Reviews
Prior to using this book had no programming background except a few seminars on python and since then have self taught myself further with code academy etc. As for R, I have been to 2 "intro" free seminars, and admit I only grasped the fundamentals of commands in the R console because the commands follow very similar structure those in a python terminal. However, in terms of R as it applies to sports, how and what commands I enter/use to answer various questions about my data I had trouble grasping without any examples to help me, a visual learner, learn from. Prior to this book no other book I've come across has provided examples with R being applied to sports. Thus this book has been a godsend. This book walks you though every command you'd use in Rstudio along with an example below it. From simple to more complex, everything function, variable, command, has an example which has helped me visualize and finally bridge the gap between the commands explained and how they are applied to the SPORTS data you have. Additionally, whats also nice is you use baseball data you can get online for free.
For me this has been the biggest help EVER . Especially if you want to use R for sports. Even if it's not baseball I would still highly recommend this book because once you get the concepts of the commands applied with baseball data, its pretty easy thereafter to play around with the same commands and packages using other sports data. I am still toying around with what I have learned in relation to other sports concepts, since pitch speed, strike effects etc unfortunately are specific to baseball. However, exploring variables for other sports datasets is so much easier now, all thanks to this book. I do hope they think about doing a a series of these books for other sports, since I am sure many other sports fans would benefit from it, including myself.
Additionally I liked how at the end of every chapter it gave the reader "further reading" suggestions, as well as great exercises to try. My only issue is there are no answers to the exercises in the book. Maybe I missed where in the book it mentioned you can find the answers, but as far as I understand, there is no answers provided to the exercise, which would have been nice. Hence why I gave it 4 out of 5 stars. I would have like to see them go through the answers, command line by command line as its entered into the console for each exercise. I hope (HINT HINT!!!!!) they will soon post this info online or something, that would be nice. Otherwise I could not be more pleased with this book!
As an example, in Chapter 8 naming of objects isn't consistent in between text passages as well as in across the book and downloadable scripts.
That said, I suppose one could say that is the nature of data analysis. A good amount of analytical work and even more debugging/data correction.
I still consider this a great resource though it could use some polish.