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With its flexible capabilities and open-source platform, R has become a major tool for analyzing detailed, high-quality baseball data. Analyzing Baseball Data with R provides an introduction to R for sabermetricians, baseball enthusiasts, and students interested in exploring the rich sources of baseball data. It equips readers with the necessary skills and software tools to perform all of the analysis steps, from gathering the datasets and entering them in a convenient format to visualizing the data via graphs to performing a statistical analysis.
The authors first present an overview of publicly available baseball datasets and a gentle introduction to the type of data structures and exploratory and data management capabilities of R. They also cover the traditional graphics functions in the base package and introduce more sophisticated graphical displays available through the lattice and ggplot2 packages. Much of the book illustrates the use of R through popular sabermetrics topics, including the Pythagorean formula, runs expectancy, career trajectories, simulation of games and seasons, patterns of streaky behavior of players, and fielding measures. Each chapter contains exercises that encourage readers to perform their own analyses using R. All of the datasets and R code used in the text are available online.
This book helps readers answer questions about baseball teams, players, and strategy using large, publically available datasets. It offers detailed instructions on downloading the datasets and putting them into formats that simplify data exploration and analysis. Through the book’s various examples, readers will learn about modern sabermetrics and be able to conduct their own baseball analyses.
- Sales Rank: #563619 in eBooks
- Published on: 2016-04-05
- Released on: 2016-04-05
- Format: Kindle eBook
Review
"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.
Most helpful customer reviews
24 of 24 people found the following review helpful.
Great way to learn how to utilize R with sports data
By JULIEK
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!
7 of 7 people found the following review helpful.
Data Management and Analysis in a Context of Baseball Sabermetrics; Creative and Well-Written Book!
By M. Allen Greenbaum
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.
7 of 7 people found the following review helpful.
Take your stat crunching to the next level
By Sibelius
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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