Moneyball by Michael Lewis

This was a great book, and my reading it solidified my identity as a HUGE Michael Lewis fan. When I was in high school and freaking out about what to pursue in college, my guidance counselor pointed me to Ken Follett's Pillars of the Earth as an example of an author marrying his love of writing with his lifelong interest in the Middle Ages. Michael Lewis, similarly, matches his love of sports with an acute acumen for economics.

Unlike just about any review I've written before, this one won't have any quotations in it. In the spirit of other other books I've loved, it has just about every page dog-earred with a comment or passage to share in the blog. Overwhelmed with this volume of comments coupled with my handwritten notes, I'll stick to a description of the book and let you trust that it is well-written.

This book is about sabremetrics, baseball statistics. It tells the story of Billy Beane, a mediocre baseball player who takes over the Oakland A's as GM, and turns the team around in a few short seasons with an incredibly low payroll. Beane does this (with the help of several statistically-minded folks, most notably Paul DePodesto) by examining what contributes to a baseball team's record and figuring out how to get those talents at below-market prices.

The book traces a history of certain players as well as of the scouting methodologies used by most teams. It also contains a large discussion of baseball economics and salaries. I most enjoyed reading about some of the unlikely heroes Beane created who were too fat, old, or unskilled in the field to play for other teams. The most hilarious scenes in the book were about Beane's tactics during the draft and trading season to get what he wanted.

His strategy, wildly successful, is based on several decades of statistical work (starting with Bill James) that has gone on quietly beside the old-school conventional wisdom of generations of sportswriters, scouts, and managers. Throughout the book these groups of people turned their noses up to Beane's approach, and many still dispute his strategy despite both his success. In 2002, for example, the A's were at the top of their division and had won more games than any other MLB team other than the Yankees, despite the A's having the lowest payroll in baseball.

Those naysayers are also up against several teams who have since begun to follow the methodology, including Toronto and Boston. The afterward of the book has some great anecdotes about people's misconceptions of the strategies employed by Beane, along with Michael Lewis' commentary on this lack of acceptance by baseball's old guard.

Some of the strategies employed by Beane are quite clever, ranging to downright amusing. For one, he would trade for a sub-par pitcher on the cheap, turn him into a closer, let him accumulate some saves to pump up his statistics, then trade him for a higher-value player. He also took advantage of the low salaries paid to newer players who contractually did not have salaries on the open market until the sixth year of their employment. Once they reached their sixth year, he would trade them for first-round draft picks which were of more value to him.

As a reasonably new baseball fan --and as a mathematician-- I was able to embrace Beane's beliefs and enjoy the David and Goliath story. The most interesting assertions on the book, some of which am still fighting with my fellow sports fans about:

-On Base Percentage (OBP) and slugging are better indicators of a player or team's ability to score runs than averages are.

-It is not worth trading outs for anything.

-Slugging and ability to draw walks stay with aging players, which ability to hit home runs does not.

-To make a difference in a team's record, base-stealers must steal bases with 70% accuracy.

-The connection between the count a batter faces and their ability to get on base is not statistically significant until the second pitch.

-Fielding is just 5% of the team's success.

To sum it up, this book was great fun. I suggest you read it during baseball season so you can "play along" with the hypotheses.