- Series: ASA-CRC Series on Statistical Reasoning in Science and Society
- Paperback: 152 pages
- Publisher: Chapman and Hall/CRC; 1 edition (August 19, 2017)
- Language: English
- ISBN-10: 1498782752
- ISBN-13: 978-1498782753
- Product Dimensions: 5.5 x 0.5 x 9 inches
- Shipping Weight: 8.5 ounces (View shipping rates and policies)
- Average Customer Review: 1 customer review
- Amazon Best Sellers Rank: #1,367,634 in Books (See Top 100 in Books)
Enter your mobile number or email address below and we'll send you a link to download the free Kindle App. Then you can start reading Kindle books on your smartphone, tablet, or computer - no Kindle device required.
To get the free app, enter your mobile phone number.
Visualizing Baseball (ASA-CRC Series on Statistical Reasoning in Science and Society) 1st Edition
Use the Amazon App to scan ISBNs and compare prices.
Fulfillment by Amazon (FBA) is a service we offer sellers that lets them store their products in Amazon's fulfillment centers, and we directly pack, ship, and provide customer service for these products. Something we hope you'll especially enjoy: FBA items qualify for FREE Shipping and Amazon Prime.
If you're a seller, Fulfillment by Amazon can help you grow your business. Learn more about the program.
Frequently bought together
Customers who viewed this item also viewed
"Jim Albert has a new book out. It’s called Visualizing Baseball and it’s a treat. It is a pretty short book―just 135 pages―and many of those pages are filled with graphs rather than text. Jim is adept at combining words and visuals. His prose is pointed and precise, and he doesn’t waste time 'sabersplaining.' Instead, he articulates a concept and then shows it on a graph. Both parts of your brain are engaged, your understanding deepens, and the lessons carry longer.The graphs start out simple and grow in complexity as the content becomes more complex. Moving from simple scatterplots with fitted lines, Albert moves onto graphs with elaborate labels and changes the size of dots based on some underlying values. He inserts box plots, classic PITCHf/x graphs, density graphs, isobars and violin plots, to name a few techniques. It sounds overwhelming, but each graph builds on a previous one, and it all makes sense as you move along.
Most importantly, Albert doesn’t clog his graphs with lots of graph junk and needless color. The only color is blue―everything else is in black/gray scale. So many graphs these days seem to be built to impress other graphic artists instead of educating the reader. This book is a welcome antidote to that trend."
~The Hardball Times
"The primary aim of the book is well executed. In almost all cases the graphics are well thought out, and quickly communicate characteristics in the data, that would be difficult to convey using tables or summary statistics. The quality of the diagrams and the printing is very good. Each diagram comes with a clear explanation and many of the results are demonstrated usingwell known players."
~Journal of Statistical Software
"This is a book written by a statistician for the stats enthusiast about baseball. I say stats enthusiast and not statistician because little to no stats experience is required to understand the book. Likewise little expertise about baseball is assumed...The big appeal to Visualizing Baseball is the graphs...I learned a lot from Visualizing Baseball, and that's impressive given that the book is only 142 pages. I'm glad I spent the time to read it."
~Jack Davis, Simon Fraser University
About the Author
Jim Albert is professor of statistics at Bowling Green State University. He is the author or editor of ten books, including Analyzing Baseball Data with R, Curve Ball, and the Handbook of Statistical Methods and Analyses in Sports.
Showing 1-1 of 1 reviews
There was a problem filtering reviews right now. Please try again later.
things. So, if you want a view on how far this book tells you anything
new about baseball, you must hope for other reviews. Let's understand
that Albert is focused entirely on visualizing data on professional
baseball in the United States. My concern is just with visualizing data
-- almost any kind of data -- and thus with how far this book will be
interesting or useful to others with that concern.
Albert gives a little attention to people who don't know about baseball,
but not much, and I really do forgive him. Reversing roles and imagining
a book about cricket, which I understand a bit better, I would say that
explaining cricket in a book would be futile unless readers have played
it or at least watched several matches in the company of someone who
understands it. If the title puts you off, and you proceed, it's on your
Apart from its inherent appeal, baseball is attractive to many who are
statistically minded because so much high quality data are readily
available for a long period. In graphing such data, Albert lives up to his
day-time job as an excellent academic statistician. He uses more
advanced methods as well as very simple methods to visualize baseball
data. But what's a little puzzling is that through most of the book
there is almost no explanation of what is being done statistically. It's
made clear that all the graphs were produced in the free statistical
software R. There is mention of a linked website. Amazon protocol
forbids me from giving a precise URL, but do a net search for
"Visualizing Baseball GitHub". The material there is just a few code
examples with brief comments, not a complete statistics and R companion.
In the first two chapters Albert shows historical data on the sport and
on individual players. Smooth curves complement summary data such as
mean runs per game for each year from 1901 to 2015, or performance
trajectories for Babe Ruth or Barry Bonds or various other baseball
greats in terms of their age in each season over their careers. For the
statistically minded, the keyword "loess" is informative in the website
material as explaining the smooth curves. Otherwise I can't easily guess
how far the results will seem clear to those unfamiliar with loess.
There is no discussion of how much data should be smoothed, let alone
precisely how it was done. We have here an interesting experiment. After
all, atlases may use a variety of map projections without assuming that
people understand the spherical trigonometry that underlies them. Weather
forecasters in the media don't explain how the forecasts were produced.
So also, statisticians don't always have to explain their white magic.
The main positive feature about the book as a whole is that Albert's
graphs are mostly big and bold and very clear. They might well provide
inspiration for quite different projects for say students, or
scientists, or people in business. At the simplest level, there are some
nice examples of what are now often called Cleveland dot charts,
although readers would need some detective work to find further
discussion. (One of Cleveland's books is in the small reference list.)
Further, Albert introduces some graphs that appear to be new, at least
in their application to baseball.
There are small points that do cry out for a little more detail or
slightly different choices. The box plot on p.58 follows just one of
many conventions and is likely to seem puzzling unless that is
explained. I see no data in my copy of the book in the graph on p.59.
Graphs showing hitting direction for home runs seem perverse in not
labeling 90 degrees as the direction of second base (pp.71, 72, 73,
In later chapters Albert's self-denying ordinance about not explaining
gives way and the text is occasionally sprinkled with equations and
brief technical explanations (generalized additive models on p.79;
Bradley-Terry model on p.104; geometric distributions on p.127). As the
publishers say in their puff, the book is written for several types of
readers. Other way up, anyone bemused by early chapters as not
explaining enough is likely to feel more annoyed or lost as the book
Unsurprisingly then, this book will appeal most to people not just
interested in baseball, but also technically minded enough to have a
strong sense of what is being done. It might fit well, for example, as
supplementary reading in statistics courses, presuming some level of
tolerance from those indifferent to sports trivia.