Understanding Sabermetrics: An Introduction to the Science of Baseball Statistics

by Gabriel B. Costa, Michael R. Huber, John T Saccoman

Interest in Sabermetrics has increased dramatically in recent years as the need to better compare baseball players has intensified among managers, agents and fans, and even other players. The authors explain how traditional measures--such as Earned Run Average, Slugging Percentage, and Fielding Percentage--along with new statistics--Wins Above Average, Fielding Independent Pitching, Wins Above Replacement, the Equivalence Coefficient and others--define the value of players. Actual player statistics are used in developing models, while examples and exercises are provided in each chapter. This book serves as a guide for both beginners and those who wish to be successful in fantasy leagues.

Genres: Baseball, Sports, Nonfiction

220 pages, Paperback

First published December 1, 2007

Reviews

[Aaron · Rating 4 out of 5]

While other reviewers have described this as essentially a math textbook, I think readers with high school–level math skills can follow most, if not all, of the content. There are some frustrating errors—such as the incomplete R code on page 150, which will not run—and some glaring typos (e.g., page 83: “There does not appear to be a high correlation between linear weights offensive runs and wins above replacement.” Wrong!).

That said, there are definitely strong sections in the book, including a lengthy discussion on building a Wins Above Replacement model using publicly available data. I liked the book enough to purchase the next two sabermetrics volumes in the series, but I do wish it had a better editor and a bit more focus on key topics rather than, say, debating whether Gil Hodges belongs in the Hall of Fame. I may use a couple of chapters from this text for a university-level Sports Analytics course.

[Eric Parsons · Rating 2 out of 5]

After a while, I asked myself, "why am I reading this?" I love baseball. I agree that the traditional stats (BA, ERA, and the like) are not terribly useful overall. I agree that the SABR stats are more descriptive and predictive. My own favorite team, the Pittsburgh Pirates, were re-built on them for 2012-2016. (See Travis Sawchik's "Big Data Baseball.") However, even though I'm a math and baseball guy, this book was somewhat pointless. With the extra stats, there wasn't much discussion of the validity of them, so why bother?

[Daniel Archer · Rating 4 out of 5]

Interesting read, though quite literally a math textbook wrapped around sabermetric concepts. I’m quite satisfied to leave the detailed math to others and just consume the stats they create, as long as I understand what they’re doing and how they got there.

[Creed Anderson · Rating 4 out of 5]

I don’t think I really learned that much about sabermetrics besides finally understanding WAR. However, just hearing these guys write about it gives me so much more respect and love for the game. A surprisingly enjoyable read.

[Ivan Taylor · Rating 4 out of 5]

This is a small textbook for a short university math course. It is a good introduction to sabermetrics. Unfortunately, there are many typographical errors that make reading it difficult.