I've read it - it's inspired a short series I shall be posting shortly.

ETA: not a chapter-by-chapter review, though I might do that too; rather, I focus on one small aspect (the "waterline" model discussed in connection with the poker bubble) and use it to illustrate something else (tips and tricks in forecasting).

I read it - I think the way I described it to the Less Wrong DC group was, "like an introductory Less Wrong article, except a book."

To elaborate a bit: * Pro: The writing is clear and engaging. * Pro: There's a lot of good case-study material and a bit of useful theory connecting them. * Con: The book is lacking in technical detail regarding Bayesian thinking (and his formulation of Bayes' Theorem is kinda messy). * Con: Nate Silver is more prone to claim successful predictors as being Bayesian thinkers than he justifies in the text. * Con: There are a few glaring typos in the first printing (e.g. a table on which the "Correlated" and "Uncorrelated" column headers are switched).

It's easy to read, and it's worth reading for the case studies, but I'd probably put it at a "Borrow" rather than a "Buy" recommendation.

ETA: The review you posted is good.