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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Modeling: insights from the pros

It's been a busy few weeks, so I've spent Super Bowl Sunday* catching up on the various blogs that I try to follow. A couple of posts from Andrew Gelman and Aleks Jakulin caught my eye: Why can't I be more like Bill James, or, The use of default and default-like models and the two-part Model Makers' Hippocratic Oath (Part 1 and Part 2).

All these posts are worth reading in their entirety, but they all boil down to the quote from George E.P. Box: "Essentially, all models are wrong, but some are useful." Knowing (or if you're the author, admitting) the limitations of the model is the most important to understanding how useful a model might be.

*Pitchers and catchers start reporting for spring training one week today!

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Andrew Gelman's "5 Books"

Andrew Gelman is one of the most interesting (IMHO) social scientist/statisticians in The Academy. Not only does he have serious statistical chops (he co-authored Bayesian Data Analysis with Carlin, Stern, and Rubin), but he also has published a raft of papers on voting patterns. His blog Statistical Modeling, Causal Inference, and Social Science -- written with his colleague Aleks Jakulin -- offers wide ranging commentary on everything from statistical theory and philosophy, to R (the statistical software), to all manner of social statistics.
Gelman was recently approached by The Browser to suggest five books on how people vote in the U.S., but instead he provided a list of five excellent books about statistics.  #1 on his list:  Bill James’ Baseball Abstracts 1982-1986.

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Good math, bad statistics

In the past few days, a pair of posts on other blogs caught my attention -- they seem to be coming at the same issue from different directions.

First, William R. Briggs posted "Statistics Is Not Math" (November 16, 2010). Then, Tango over at The Book posted "Detrending: when statisticians attack!" (November 24, 2010). I responded to the Tango post (comment #4), but I would like to here elaborate further.

One of the things that jumped out at me from Briggs' post was the statement that "Statistics rightly belongs to epistemology, the philosophy of how we know what we know. Probability and statistics can even be called quantitative epistemology." In other words, statistics is useful only if we have some understanding of the subject matter at hand. No amount of fancy math will help our understanding if we do not start our research with some knowledge of the topic.

In the "Detrending" post, Tango links to an unpublished (in the academic sense that it's not been published in a peer-reviewed journal) paper, by three physicists, Alexander M. Petersen , Orion Penner, and H. Eugene Stanley, entitled "Detrending career statistics in professional baseball:

Accounting for the steroids era and beyond". I may offer a longer critique of this paper at a later date, but the first thing that jumps out is an apparent ignorance of The Literature (i.e. what's been written earlier about the topic -- baseball -- from a statistical basis). This leads the authors to make conclusions that have been supported elsewhere (for example, pitcher wins are not a good measure of pitcher performance, or that standardizing allows for inter-season comparisons).

There's lots of fancy maths (some of which isn't as fancy or new-fangled as the authors seem to think) and plenty of Greek letters, but in the end it doesn't add a great deal to our understanding of baseball.

This article serves as a reminder that when we are assessing the quality of any sabermetric writing, we need to consider two factors:

1. Is the author using the appropriate statistical tools and interpreting the mathematical results correctly?

2. Does the author understand the game, including how baseball has evolved and the analytic literature that has been written over the past 50 years?

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