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11 of 11 people found the following review helpful
4.0 out of 5 stars Great way to learn how to utilize R with sports data, January 26, 2014
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This review is from: Analyzing Baseball Data with R (Chapman & Hall/CRC The R Series) (Paperback)
I'm admit am not really into baseball analytics. However, I have been slowly getting into analytics for other sports, and really wanted to start using R to explore my data. I am pretty new to R, so I had no idea what to do with 200+ variables had collected in excel, in Rstudio. So I turned to texts and other books but struggled. A majority of the time the books I read used business data etc for examples which didn't really help me personally, grasp how I could replicate the same commands using my sports data. Trying to find a book that has programing and sports in it is like finding a needle in a haystack. This book was the only book that came up in search results when I entered sports + analytics. When I stumbled upon it, despite it being about baseball, I bought it immediately after I recognized Jim Alberts name (from JQAS), and I am so glad I did. For those sports data lovers out there who want to understand how to use R to analyze your datasets rejoice! because this is only book you will probably need!!!!

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!
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14 of 15 people found the following review helpful
5.0 out of 5 stars Terrific, November 1, 2013
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This will be essential reading for those looking to learn R as it specifically relates to baseball analytics. The book explores various datasets, explains with clarity how they are designed, and discusses how they can be utilized to do baseball specific analytics. Easily the most comprehensive resource available for the aspiring baseball analyst as it compiles much of the work previously located in hidden corners of the blogosphere into one easily understandable and accessible book. A must buy.
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6 of 7 people found the following review helpful
5.0 out of 5 stars Excellent, January 26, 2014
By 
Devon (Harrisonburg, VA) - See all my reviews
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This review is from: Analyzing Baseball Data with R (Chapman & Hall/CRC The R Series) (Paperback)
Probably unlike most people who will buy this book, I am more well versed in R than I am Sabermetrics. I bought this book to teach me more about baseball statistics, and I figured it would be worth it considering Jim Albert's involvement.

I have taken formal classes in R in graduate school, and let me tell you this book was a dynamite review and I think even better at teaching basic coding and packages than some of the books solely dedicated to R out there. It also directed me to some databases I was unfamiliar with and where to find specific datasets, which is amazing.

This is a must buy for those looking to conduct statistical analyses on their favorite team while utilizing free software.

Looking forward to next season.
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2 of 2 people found the following review helpful
5.0 out of 5 stars Quality book, December 26, 2013
I confess to having no interest in baseball - why check out the book then? For the chance of finding a nice, transferable statistical analysis or visualization - but if you do, "Analyzing baseball data with R" is an excellent learning aid, offering wealth of information and effectively teaching R via a sequence of substantial data-exploration exercises. I like the book's attention to its datasets and data sources - note also the sections on parsing online data and storing data in MySQL database - and give extra points for good coverage of "ddply", "lattice" and "ggplot2" R packages.
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1 of 1 people found the following review helpful
5.0 out of 5 stars What a great way to learn the R programming language!, September 24, 2014
This review is from: Analyzing Baseball Data with R (Chapman & Hall/CRC The R Series) (Paperback)
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R is an open source programming language intended for statisticians and data miners. Applying R to processing baseball statistics is a brilliant move. Even if you have no interest in baseball or the vast body of statistics surrounding it, the data will be familiar: Babe Ruth and Hank Aaron, homeruns, hits and so on. Even non-fans will have some familiarity with baseball.

The authors apply R to a variety of statistical analyses of baseball statistics. I knew that there were people who followed baseball closely, but I was frankly surprised by just how seriously so many people take analysis of baseball statistics. There’s even a name for the pursuit: sabermetrics. (As Explained on Wikipedia: “the empirical analysis of baseball, especially baseball statistics that measure in-game activity. The term is derived from the acronym SABR, which stands for the Society for American Baseball Research. It was coined by Bill James, who is one of its pioneers and is often considered its most prominent advocate and public face”.)

The book has a very different structure than most programming tutorials. No time is spent preparing you with fundamentals: the authors just dump you into analyzing baseball statistics with R. Boom. Open the book and you’re there. If you are aready a programmer, it’s a good approach – but if you’re not, I think you’ll possibly encounter problems.

I use Excel for data analysis and mining, can brute force my way through several dialects of Basic and sort of hold my own with Python.

With this book, picking up a basic working knowledge of R is not only easy, but fun and interesting.

Jerry
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5.0 out of 5 stars Data Management and Analysis in a Context of Baseball Sabermetrics; Creative and Well-Written Book!, October 31, 2014
This review is from: Analyzing Baseball Data with R (Chapman & Hall/CRC The R Series) (Paperback)
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A relatively fun way to learn the database manager/statistical software "R." the authors take the novel approach of teaching the language within the context of baseball statistics. Become a sabermetrician and become the envy of your neighborhood!

Those who favor the "learning R" aspect will be reawarded more than baseball aficionados. The latter will be satisfied with some of the fairly arcane learning examples (how well did Warren Spahn pitch in the post-war era; what are the relatively trajectories by age of the home run totals of Bonds, Ruth, and Aaron; predicting post-season performace with Markov chain probabilities-we'll have to try that with the 2014 Giants sometime), but the focus is on learning R, not learning baseball lore. For example, the baseball problems don't containa lot of context: Bonds' probably steroid use is not mentioned; it's irrelevant to the main topic.

"R" is not an intuitive language. though moreso than SQL. In my experience it is increasingly used as either a first-line or adjunctive statistical programming language alongside the most popular (social science) packages: SAS, SPSS, Mplus, and, for database construction, SQL (which is also part of SAS using Proc SQL). It uses elements of SAS /SPSS with a few of the more manageable conocepts from SQL (NO, I'm not a fan of SQL, but then, I came rather late to it.).

While certainly not a "Book for Dummies" (and really, must beginners put up with that...), some experience with databases will bevery helpful. You'll also get more out of the book with some statistical knowledge, but that's not necesary. It seems like most languages have their own nomenclature for the same methods and concepts, and the authors excel at presenting R's vernacular in a slow, patient style that will help you grasp the fundamentals, and they use screen shots and drawings to help guide you through the R graphic interface. Here's an example of their pedagogy from page 32:

Type directly into the console window (they xshow where this is, Spahn's games won for his 7 seasons with the Boston Braves. L < -(5,10,12,14,17,14,10) to create what R calls a "vector." (No, not all examples concern the great Spahn!).

The R "vector" is simply the SAS/SPSS "variable" and the SQL "column!" Five pages later, we're introduced to the concept of matrices so that we can save our data rather than repeatedly entering it (like a SAS datastep or an SQL table or view), and on page 41, they introduce the concept of "scripts" (a tool which seems similar to a SAS macro.)

Not only is this well organized, logical, and generally clear, but the book explains where to download a free "R" program and how to import some web-basedbaseball-oriented datasets (with actual data; this *will* fascinate baseball people!) on which to practice. There are practice exercises (although I didn;t see any ranswers), a dual index (one for commands and one for topics), and over 53 refences for further exploraion of R and related material. Statistical heavyweights can practice logistic and linear regression (R uses a General Linear Model), exploring moving averages and moving average plots (the book has a large amount of material on R graphics), quadratic model fit, how to calculate a wqeighted on-base percentage, and slugging percentage.

Although some of this may sound rather daunting, it's still an excellent book for the relative beginner, and you don't need to understand every concept to benefit. If you're completely new to statistical and database concepts, you should probably go with something simpler, but others will learn a lot from this refreshingly interesting and well-written book. When you get bogged down in it, go out and play some ball; it'll help clear your head.
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5.0 out of 5 stars You will learn a lot about R, October 4, 2014
This review is from: Analyzing Baseball Data with R (Chapman & Hall/CRC The R Series) (Paperback)
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If you're looking for an introductory book on R that starts with the basics then seamlessly progresses into more substantive programming and analysis topics and tutorials, you may want to consider this book.

The baseball data used in this book are all from well-established online repositories and instructions on where and how to download them are provided in the book. Instructions on where and how to download the code and data used in the tutorials are also in the book.

The initial tutorials will teach you how to install R and supporting software, such as RStudio, onto your computer (all software used in the book will work on Windows, Macintosh or Linux operating system). These are then followed by tutorials on R data structures, how to manipulate them, and access their components. You will then learn how to read and load data files into R data sructures, process and modify them, and save them back to flat files.

After these foundational topics, you will learn how to write R functions, and how to graph your data using basic packages built into R as well as more advanced packages that can be installed into your R environment, such as "lattice" and "ggplot2."

The code you will learn how to write will help you find answers to different types of analytical questions, fit curves or model to your data, get a sense of how "good" your data models are, and even simulate a baseball game or season. There is also a chapter on how to store large data sets into a relational database such as MySQL and how to access and process such relational data in R using special packages that can be installed into your R environment.

The well-written and easy to follow tutorials are staged very well so that the progression from basic to more advanced makes a lot of sense. Each chapter ends with suggested readings and exercises, but answers to those exercises are not included in the book.
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4.0 out of 5 stars Take your stat crunching to the next level, November 7, 2014
By 
Sibelius (Palo Alto, CA USA) - See all my reviews
This review is from: Analyzing Baseball Data with R (Chapman & Hall/CRC The R Series) (Paperback)
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First thing first - if you have little knowledge/interest in baseball but are considering picking this book up to learn 'R' my advice is to skip this book and find another source. Getting the most out of this volume requires a deep knowledge of the game in order to fully understand the context of the exercises and the way that subtle, almost imperceptible variations in said data will make all the difference in validating a particular hypothesis. For baseball stat junkies looking to take their analysis to the next level (beyond Excel at least) this is a very well laid out and presented guide that showcases the power of 'R' through the infusion of rich and readily available sources of preexisting historical data.

The book starts off with helpful chapters on the installation of 'R' and plugging data sets into the system. Later chapters then take on a practical approach to demonstrate the utilization of 'R' in tackling some of the more common Sabermetric patterns including streaky behaviors and run expectancies. Closing chapters take on more technical complexity as the exercises add additional layers of data and software to the 'R' package (the chapter on incorporating MySQL was of particular interest). All in all a good no fuss, practical guide on the utilization of 'R' to further grind your baseball metrics down to the nitty gritty.
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5.0 out of 5 stars Learn to be a sabermetrician!, September 18, 2014
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This review is from: Analyzing Baseball Data with R (Chapman & Hall/CRC The R Series) (Paperback)
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I've been a fan of sabermetrics (baseball analytics) since Bill James' first nationally published BASEBALL ABSTRACT in 1982 and have read Jim Albert's and Jay Bennett's CURVE BALL (great book, by the way.) Since then, and increasingly so, there has been a plethora of valuable sabermetric websites that have been launched, filled with PITCHf/x analysis, batted-ball scattergrams, fielding plots, aging curves, and win expectancy calculations, etc.

With this valuable effort by Jim Albert and Max Marchi, you, too, can learn to take a data set of your choosing and learn to calculate various tables, plots, curves, and scattergrams through the use of R.

The authors take you step-by-step with R to show how to mine and analyze a data set, and give clear examples of the outcomes. If you are merely interested in reading essays or blogs concerning sabermetric revelations, this isn't the book for you (read CURVE BALL instead). This is a valuable resource and a great learning tool for those of you who want to datamine and present the outcomes . By the end of the book, you will be a practicing sabermetrician.

Highly recommended!
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5.0 out of 5 stars Get This Book If You Want To Learn R. Even if you have NO interest in Baseball :), September 30, 2014
This review is from: Analyzing Baseball Data with R (Chapman & Hall/CRC The R Series) (Paperback)
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I highly recommend this book as a primer for R, even though I may be the last person in the world who would be interested in baseball. It gives a clear and interesting and easily understood introduction to R. First basic statistics and the R language are introduced in a clear and well exemplified fashion. A variety of built-in and external (e.g. in ggplot ) graphical techniques and visualization tools are presented. A variety of more advanced techniques (e.g. Markov Chains) are presented and well-explained.
Finally, the means to interact with databases such as MySql and importing foreign data formats is given, exemplified and explained.

This is one of the best primers to basic and not-so-basic R usage, and the examples are interesting and well explained even if you have no interest in sport.
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Analyzing Baseball Data with R (Chapman & Hall/CRC The R Series)
Analyzing Baseball Data with R (Chapman & Hall/CRC The R Series) by Jim Albert (Paperback - October 29, 2013)
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