Storytelling with Data: consumer alert

In March 2016 I gave a favourable review to a newly published book, Storytelling with Data by Cole Knaflic. Today (2017-04-03) she posted a new blog entry, "the book you're holding might be a fake!", in response to the discovery that poor-quality pirate editions of her books are available for purchase.

Knaflic's response has been exemplary: notifying people who bought the book what to look for in the knock-offs, and how to exchange the book for a proper copy. If you have a copy, go to the linked site above and check your copy against the description. (Fortunately, my copy is legit.)

This problem has alerted me to something I neglected to mention in my original review: the high quality of the physical book. The paper has a soft sheen which allows the print and graphics to stand out, the colours are sharp and consistent, and printing is clear.

A year later, my high opinion of this book has not shifted.

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Book review: Storytelling With Data

by Cole Nussbaumer Knaflic (2015, Wiley)

The Sabermetric bookshelf, #4

One of the great strengths of R is that there are some robust (and always improving) packages that facilitate great data visualization and tabular summaries. Beyond the capabilities built into the base version of R, packages such as ggplot2 (my favourite), lattice, and vcd and vcdExtra extend the possibilities for rendering charts and graphs, and a similar variety exist for reproducing tables. And accompanying these packages have been a variety of fine instruction manuals that delineate the code necessary to produce high-quality and reproducible outputs. (You can’t go wrong by starting with Winston Chang’s R Graphics Cookbook, and the R Graph Catalog based on Naomi Robbins’s Creating More Effective Graphs, created and maintained by Joanna Zhao and Jennifer Bryan at the University of British Columbia.)

Let’s call these the “how” resources; once you’ve determined you want a Cleveland plot (which are sometimes called “lollipop plots”—please, just stop it), these sources provide the code for that style of chart, including the myriad options available to you.

Elsewhere, there has been a similar explosion in the number of books that build on research and examples as to what makes a good graphic. These are the “what” books; the authors include the aforementioned William Cleveland and Naomi Robbins, and also include Stephen Few and Edward R. Tufte. Also making an appearance are books that codify the “why”, written by the likes of Alberto Cairo and Nathan Yau.

The recently published Storytelling with Data: A Data Visualization Guide for Business Professionals by Cole Nussbaumer Knaflic falls into the latter category, and it’s one of the best I’ve seen to date. Although the subtitle indicates the intended audience, I believe that anyone involved in creating data-driven visualizations would benefit from reading and learning from it.

The book is relatively software agnostic, although Nussbaumer Knaflic recognizes the ubiquity of Excel and has used it to produce the charts in the book. She also provides some sidebar commentary and tools via her website http://www.storytellingwithdata.com/ specifically using Excel. For R users, this shouldn’t pose a particular challenge or barrier; the worst-case scenario is that it provides an opportunity to learn how to use R to replicate the book’s examples.

One of the strengths of the book is that Nussbaumer Knaflic takes the approach of starting with a chart (often a real-life example published elsewhere), and then iterates through one or more options before arriving at the finished product. One instance is the step-by-step decluttering of a line graph, which becomes substantially improved through a six step process. This example re-appears later, first in the chapter on the use of preattentive attributes and then again in the chapter titled “Think like a designer”. This approach reinforces the second of Nussbaumer Knaflic’s tips that close the book, “iterate and seek feedback”.

Nussbaumer Knaflic also introduces the Gestalt Principles of Visual Perception, and provides vivid examples of how these principles play out in data visualizations.

All of the discussion of graphics is wrapped in the context of storytelling. That is to say, the data visualization is always in the service of making a point about what the data tell us. In the context of business, this then translates into influencing decisions. The chapter “Lessons in storytelling” falls almost exactly in the middle of the book; after we’ve been introduced to the principles of making good data visualizations, Nussbaumer Knaflic gives us a way to think about the purpose of the visualization. With all of the pieces in place, the remainder of the book is focussed on the applications of storytelling with data.

The book is supported with Nussbaumer Knaflic’s site storytellingwithdata.com, which includes her blog. Check out her blog entry/discussion with Steven Few “Is there a single right answer?”), and some makeovers (in the Gallery) where she redraws some problematic charts that have appeared in the public domain.

All in all, Cole Nussbaumer Knaflic’s Storytelling with Data is a succinct and focussed book, one that clearly adopts and demonstrates the enormous value of the principles that it espouses. Highly recommended to anyone, of any skill level, who is interested in making effective data visualizations, and the effective use of those visualizations.

Cole Nussbaumer Knaflic’s Storytelling with Data: A Data Visualization Guide for Business Professionals was published in 2015 by Wiley.

Cross-posted at monkmanmh.github.io

References

Note: the authors of the following books have all published additional books on these topics; I’ve simply selected the ones that most closely fit with the context of this review. All are recommended.

• Alberto Cairo, The Functional Art: An Introduction to Information Graphics and Visualization (2013) New Riders.
• Winston Chang, R Graphics Cookbook (2012) O’Reilly.
• William S. Cleveland, Visualizing Data (1993) Hobart Press.
• Stephen Few, Signal (2015) Analytics Press.
• Michael Friendly and David Meyer, Discrete Data Analysis with R: Visualization and Modeling Techniques for Categorical and Count Data (2016) CRC Press.
• Naomi B. Robbins, Creating More Effective Graphs (2004) Reprinted 2013 by Chart House.
• Edward R. Tufte, The Visual Display of Quantitative Information (2nd edition, 2001) Graphics Press.
• Hadley Wickham, ggplot2: Elegant Graphics for Data Analysis (2nd edition, 2016) Springer.
• Nathan Yau, Visualize This: The FlowingData Guide to Design, Visualization, and Statistics (2011) Wiley.

Book Review: Analyzing Baseball Data with R

by Max Marchi and Jim Albert (2014, CRC Press)

The Sabermetric bookshelf, #3

Here we have the perfect book for anyone who stumbles across this blog--the intersection of R and baseball data. The open source statistical programming environment of R is a great tool for anyone analyzing baseball data, from the robust analytic functions to the great visualization packages. The particular readership niche might be small, but as both R and interest in sabermetrics expand, it's a natural fit.

And one would be hard pressed to find better qualified authors, writers who have feet firmly planted in both worlds.  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.

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. 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).  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. Both employ plenty of baseball examples in their explanations of statistical analysis using R.

In Analyzing Baseball Data with R Marchi and Albert consolidate this joint expertise, and have produced a book that is simultaneously interesting and useful.

The authors takes a very logical approach to the subject at hand. The first chapter concerns the three sources of baseball data that are referenced throughout the book:
- the annual summaries contained with the Lahman database,
- the play-by-play data at Retrosheet, and
- the pitch-by-pitch PITCHf/x data.
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 reader first encounters R in the second and third chapters, titled "Introduction to R" and "Traditional Graphics". 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. 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.

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. These chapters will most certainly help them climb the steep learning curve faced by every neophyte R user.  (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. Not all valuable data are big data.)

From that point on, the book tackles some of the core concepts of sabermetrics. This includes the relationship between runs and wins, run expectancy, career trajectories, and streaky performances.  As the authors work through these and other topics, they weave in information about additional R functions and packages, along with statistical and analytic concepts.  As one example, one chapter introduces Markov Chains in the context of using R to simulate half inning, season, and post-season outcomes.

The chapter "Exploring Streaky Performances" provides the opportunity to take a closer look at how Analyzing Baseball Data with R compares to Albert's earlier work.  In this case, the chapter uses moving average and simulation methodologies, providing the data code to examine recent examples (Ichiro and Raul Ibanez).  This is methodologically similar to what is described in Curve Ball, but with the addition of "here's the data and the code so you can replicate the analysis yourself".  This approach differs substantially from the much more mathematical content in Albert's text Bayesian Computation with R, where the example of streaky hitters is used to explore beta functions and the laplace R function.

Woven among these later chapters are also ones that put R first, and use baseball data as the examples. A chapter devoted to the advanced graphics capabilities of the R splits time between the packages lattice and ggplot2. The examples used in this chapter include  visualizations that are used to analyze variations in Justin Verlander's pitch speed.

Each chapter of the book also includes "Further Reading" and "Exercises", which provide readers with the chance to dig deeper into the topic just covered and to apply their new-found skills. The exercises are consistently interesting and often draw on previous sabermetric research.  Here's a couple of examples:

• "By drawing a contour plot, compare the umpire's strike zone for left-handed and right-handed batters. Use only the rows of the data frame where the pitch type is a four-seam fastball." (Chapter 7)
• "Using [Bill] James' similarity score measure ..., find the five hitters with hitting statistics most similar to Willie Mays." (Chapter 8)

The closing pages of the book are devoted to technical arcana regarding the data sources, and how-to instructions on obtaining those data.

The authors have established a companion blog (http://baseballwithr.wordpress.com/), which has an expansion of the analytics presented in the book.  For example, the entry from December 12, 2013 goes deeper into ggplot2 capabilities to enhance and refine charts that were described in the book.

Analyzing Baseball Data with R provides readers with an excellent introduction to both R and sabermetrics, using examples that provide nuggets of insight into baseball player and team performance. The examples are clear, the R code is well explained and easy to follow, and I found the examples consistently interesting. All told, Analyzing Baseball Data with R will be an extremely valuable addition to the practicing sabermetrician's library, and is most highly recommended.

Additional Resources

Jim Albert and Jay Bennett (2003), Curve Ball: Baseball, Statistics, and the Role of Chance in the Game (revised edition), Copernicus Books.

Jim Albert and Maria Rizzo (2011), R by Example, Springer.

Jim Albert (2009), Bayesian Computation with R (2nd edition), Springer.

An interview with Max Marchi, originally posted at MilanoRnet and also available through R-bloggers

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Sabermetrics primer

Phil Birnbaum, author of the Sabermetric Research and the editor of SABR's "By the Numbers", has written a primer on the topic with the title "A Guide to Sabermetric Research" that appears at the SABR site. This should be the first stop for anyone who wants to find out more about the field of sabermetrics, and a good read for those already active.

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The Bayes Ball Bookshelf, #2

Baseball Analyst, 1982-1989 (Bill James, publisher and editor)

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SABR is now hosting -- the the blessing of Bill James, and through the work of Phil Birnbaum -- the complete Baseball Analyst.  Between 1982 and 1989, Bill James published 40 issues of Baseball Analyst, which in retrospect is now recognized as the launch pad for some fundamental thinking about using quantitative approaches to understand baseball.

The initial issue got off to a great start, with an article about fielding by Paul Schwarzenbart. In his introduction to the issue, James writes that the article "demonstrates that fielding statistics, like batting and pitching but apparently even more so, are the products in part of circumstances as well as men." This is a topic that, 30 years later, continues to provide plenty of fodder for analysis (e.g. this blog post from a month ago by Tangotiger, "Not all fielding opportunities are created the same").

In later issues, there are articles covering the usual parade of topics: clutch hitting, ballpark effects, how much young pitchers should work, ageing of ball players, and of course movie reviews.

There's also familiar names: Pete Palmer, Phil Birnbaum, and Bill James himself.

All in all, Baseball Analyst is an interesting time capsule. The tools the sabermetric community use to communicate have shifted -- when was the last time you subscribed to a magazine produced on a typewriter and mimeograph? But more importantly, it demonstrates how thinking about these topics has shifted. This shift is both because of further research (we know more than we used to) and because of the proliferation of data and cheap computing power

But it also shows that in spite of 30 years of analysis, there are still many questions unresolved.

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Bayes book

I recently learned about a new book by Sharon Bertsch McGrayne, The Theory That Would Not Die: How Bayes' Rule Cracked the Enigma Code, Hunted Down Russian Submarines, and Emerged Triumphant from Two Centuries of Controversy.

I haven't got a copy yet, but based on a couple of reviews, it seems like it's going to be a good read. The review in Significance magazine describes it thus: "At times reading like a historical account, at times like investigative journalism, at yet other times like a statistical commentary."

Other reviews:  New York Times Sunday Book Review, Boston Globe, and Nature (subscription required for on-line access).

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The Bayes Ball Bookshelf, #1

The Numbers Game: Baseball's Lifelong Fascination with Statistics, by Alan Schwarz. 2004, St. Martin's Press.

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In The Numbers Game, Alan Schwarz presents a well-written and tidy history of the development and evolution of the statistics that record the history of the game. Or more accurately, it's a history of baseball, and its evolution over the past century and a half, from the perspective of the numerical record and analysis of the game.

Thus Schwarz begins in the mid-nineteenth century, with Henry Chadwick's influence on the information that got recorded. But more importantly, Schwarz points out (and this becomes a recurring theme) that how the game was played was an influence on what got recorded. In the early days, the ball was "pitched" to the batter in a way that facilitated batting it -- and because pitching was secondary to hitting and fielding, there was no record of pitching performance. And as the game evolved, so did the numbers that recorded the game and got used to evaluate the players.

A second recurring theme is the weaving of the technical aspects of the statistics with the personal characters of those who developed and promoted various measures. This is very much a character-driven book -- we hear not only about the "why" of the statistics that were recorded, but the people who developed them and the means of recording them. So we hear about Al Munro Elias, Allan Roth's career with the Dodgers, Hal Richman's development of Strat-O-Matic, and George Lindsey's articles that appeared in academic journals beginning in the late 1950s. We also get an entire chapter devoted to the publication in 1969 of The Baseball Encyclopedia, and another to Bill James.

One of the things that jumps out to me is the impact that computers-- particularly the personal computer -- has had on the volume of statistics available, and the precision of the analysis that is now available. (And what is perhaps a topic for another day, the proliferation of analysts of varying quality.)

In The Numbers Game, Schwarz has written what may well be the single best introduction to sabermetrics. But it's not a technical manual that will tell you how to calculate any one statistic, or how another measure should be interpreted. Instead it's a lively history of major league baseball, and the numerical record and analysis that accompanies it.

Assessment: home run.