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# The Mathematization of America’s National Sport

BOOK REVIEW: The Sabermetric Revolution: Assessing the Growth of Analytics in Baseball. By Benjamin Baumer and Andrew Zimbalist, University of Pennsylvania Press, Philadelphia, 2014, 240 pages, \$26.50.

The Sabermetric Revolution: Assessing the Growth of Analytics in Baseball. By Benjamin Baumer and Andrew Zimbalist.
The peak of my involvement (if I may call it that) with baseball occurred when I was eleven. I went to all the high school games (25 cents for a season ticket), I was an ardent Red Sox fan, I listened to Fred Hoey’s radio broadcasts of the games from Fenway Park, I collected baseball cards of the famous players: Lefty Grove, Wes Ferrell, Joe Cronin, et al. This involvement vanished when I entered high school, and over the years I’ve hardly followed Major League Baseball at all. Still, baseball is the one sport I watch on TV—but only for five or ten minutes. I am bored by all the slo-mo replays.

Baseball has a discrete structure, as op-posed to the semi-continuous structure of basketball or hockey, and is therefore very amenable to mathematization. Some baseball statistics may have been around since the 1800s, and their number had grown by the time I was eleven. But more recently the mathematization of the sport has grown exponentially (as people say), and this is what The Sabermetric Revolution is all about. The “saber” in sabermetric, which derives from the acronym for the Society for American Baseball Research, refers to “the use of statistical methods to analyze player performance and game  strategy.”

The authors know their stuff. Both are professors at Smith College; Benjamin Baumer used to be a statistical analyst for the New York Mets, and Andrew Zimbalist is an economist who has written about labor and economic policies in baseball.  Their book is an in-depth successor to the bestselling 2003 Moneyball: The Art of Winning an Unfair Game, which morphed into the 2011 movie.

Acronyms a-plenty populate the pages. BABIP, DER, DIPS, ERA, HITf/x, OPB, PECOTA, and SAFE are just a sampling of the specific types of data collected and analyzed. (BABIP, for example, means batting average on balls in play.) Pythagoras makes a star appearance with the formula

$$$WPCT = RS^{2}/(RS^{2} + RA^{2}),$$\tag{1}$ where $$WPCT$$ = expected winning percentage, $$RS$$ = runs scored, $$RA$$ = runs allowed  (whatever that means). The book contains scatter diagrams, tree structures, rules of thumb. It even alludes to partial derivatives.

The increasing mathematization of baseball is indeed a revolution. “More than half of the thirty clubs have more than one person who is primarily working on analytics,” the authors write. . . . “The challenge in today’s front offices is to find enough employees who are capable of extracting meaningful information from what is quickly becoming a torrent of data.” An individual player is reduced to a vector of numbers and a league to an 8 × 3 matrix. ID compactification galore!

Watching a recent Red Sox–Minnesota Twins game, I was annoyed by the constant display of the batters’ vectors—along with such associated features as the velocity and  the arrival location of the pitches. All these pop-ups or ancillary goodies reduce the pristine purity and simplicity of the game. Under mathematization, baseball has undergone a serious metamorphosis.

One last and somber thought. Do games like baseball and football constitute “a moral equivalent of war,” to use William James’s expression? Printed opinion seems to agree with him: They are instances of war carried out by other means. But I doubt that fans would buy into this harsh evaluation. “Play ball!” is what the umpire shouts out to start things rolling. Plainly and simply, baseball is a game.

Philip J. Davis, professor emeritus of applied mathematics at Brown University, is an independent writer, scholar, and lecturer. He lives in Providence, Rhode Island, and can be reached at philip_davis@brown.edu.