![]() This includes the relationship between runs and wins, run expectancy, career trajectories, and streaky performances. Not all valuable data are big data.)įrom that point on, the book tackles some of the core concepts of sabermetrics. (It is worth noting that the "Introduction to R" chapter relies heavily on a fourth source of baseball data - the 1965 Warren Spahn Topps card, the last season of his storied career. These chapters will most certainly help them climb the steep learning curve faced by every neophyte R user. The material covered in these early chapters are things I learned early on in my own R experience, but whereas I had relied on multiple sources and an unstructured ad hoc approach, in Analyzing Baseball Data with R a newcomer to R will find the basics laid out in a straight-forward and logical progression. Some of the key R packages are also covered in these chapters, both functional packages like plyr and data packages, notably Lahman, the data package containing the Lahman database. These two chapters cover many of the basic topics that a new R user needs to know, starting with installing R and RStudio, then moving on to data structures like vectors and data frames, objects, functions, and data plots. The reader first encounters R in the second and third chapters, titled "Introduction to R" and "Traditional Graphics". The chapter doesn't delve into R, but summarizes the contents of the three data sets, and takes a quick look at the types of questions that can be answered with each. ![]() the play-by-play data at Retrosheet, and the annual summaries contained with the Lahman database, The first chapter concerns the three sources of baseball data that are referenced throughout the book: The authors takes a very logical approach to the subject at hand. In Analyzing Baseball Data with R Marchi and Albert consolidate this joint expertise, and have produced a book that is simultaneously interesting and useful. Both employ plenty of baseball examples in their explanations of statistical analysis using R. ![]() Albert's two R-focussed books, the introductory R by Example (co-authored with Maria Rizzo) and the more advanced Bayesian Computation with R, are intended as supplementary texts for students learning statistical methods. Curve Ball, written with Jay Bennett, is pure sabermetrics, and one of the best books ever written on the topic (and winner of SABR's Baseball Research Award in 2002). ![]() Jim Albert is a Professor in the Department of Mathematics and Statistics at Bowling Green State University three of his previous books sit on my bookshelf. Max Marchi is a writer for Baseball Prospectus, and it's clear from the ggplot2 charts in his blog entries (such as this entry on left-handed catchers) that he's an avid R user. And one would be hard pressed to find better qualified authors, writers who have feet firmly planted in both worlds. ![]()
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