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When It Comes to the Baseball Stat Rage, Quantification Doesn't Always Make It Science

To paraphrase Inigo Montoya, this baseball stat—“I do not think it means what you think it means”
baseball equation illustration



Credit: Matt Collins

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The lush green expanse of the outfield. The pop of horsehide ball hitting cowhide mitt. The search for hastily discarded syringes. Yes, baseball is back.

On the sacred day when I first discovered the game, the holy trinity of stats was AVG (batting average), HR (home runs) and RBI (runs batted in). Today we have OBP, OPS, UZR and WAR—and plenty more alphabet soup.

To become more nimble with these numbers, back in January I headed to a little collectible store on East 11th Street in Manhattan called Bergino Baseball Clubhouse to hear a talk by Smith College economics professor Andrew Zimbalist, co-author with former New York Mets number cruncher and current Smith visiting math prof Benjamin Baumer of The Sabermetric Revolution: Assessing the Growth of Analytics in Baseball. (That's right—they analyze the analyses. Who watches the watchmen? These guys.)

First, what on turf is sabermetrics? Legendary stat man Bill James coined the term, adding “metrics” to a slightly revised acronym for the Society for American Baseball Research, “SABR.” “Sabermetrics,” Zimbalist explained, “refers to the use of statistical analysis to understand and evaluate player performance, team strategy and front-office strategy.” Sadly, it does not refer to exactly how far down onto his sword a general manager has to fall if his team underperforms.

Sabermetrics got a big boost among the general public from the 2003 book, and later 2011 movie, Moneyball, a story of the surprisingly good 2002 Oakland Athletics. The team's key was deep stats that found low-priced and underappreciated players. And its big stat was OBP, “on-base percentage” (more or less hits plus walks divided by plate appearances), because, as the old baseball adage goes, “A walk is as good as a hit.”

In reality, a walk is clearly not as good as a hit when the hit is a home run, even though the homer counts the same as a hit in calculating batting average. Which is why one of the most popular ways to measure hitting now is OPS, “on-base plus slugging percentage,” which weights for power. Hence, Lou Gehrig's insane 1928 World Series OPS of 2.433 against the St. Louis Cardinals, off a paltry .545 batting average.

Zimbalist took issue with some of Moneyball's claims. For example, the focus on on-base percentage doesn't explain how the A's team OBP dropped from .360 in 2000, to .345 in 2001, to .339 in its annus mirabilis. But his most salient comments were for those of us who cite Albert Einstein at least as often as Theo Epstein.

Zimbalist and Baumer write in The Sabermetric Revolution that “beyond the rags-to-riches theme, [the book Moneyball] echoes another well-worn refrain in modern culture—the perception that quantification is scientific.” If all you do is count, you could tally up a million apples falling off apple trees without coming up with a theory of gravity.

At his talk, Zimbalist also criticized two of the newer stats. UZR, for “ultimate zone rating,” alleges to measure notoriously difficult-to-quantify defense. “When Derek Jeter is in the bottom 10 percent of UZR one year and the next year he's in the top 10 percent, you have to question, What is UZR measuring?” A stat that rated the degree of facial symmetry of the models Jeter dates would undoubtedly have a higher correlation from one year to the next.

Finally, there's “wins above replacement,” or WAR, which purports to figure the number of wins a player adds to his team total over a standard-issue substitute. The obvious and necessary follow-up question becomes, WAR: What is it good for? Perhaps not absolutely nothing but less than it may appear.

“These are now proprietary metrics,” Zimbalist said. “The people who generate these are selling their metrics to teams.... The numbers they're using to feed into their algorithms ... and how they weight all these different numbers—they don't tell us.... And as long as it's a black box, it doesn't make a heck of a lot of sense.”

Zimbalist pointed to David Wright, third baseman for the Mets, who received virtually identical WAR values from three different organizations. But the internal aspects of the total stat we do know about were all over the place. So the convergence on a similar value is reminiscent of the four statisticians who went duck hunting. All four missed the duck, but because the average of their shots was where the duck was, they announced, “We got him!” Yeah, about as much as the Cards' pitching staff got out Lou Gehrig.

This article was originally published with the title "Numbers Game."

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