Moneyball Lewis, Michael (best biographies to read .TXT) 📖

hundred (.400) and slugging percentages between three hundred and fifty (.350) and five hundred and fifty (.550).

Baseball fans and announcers were just then getting around to the Jamesean obsession with on-base and slugging percentages. The game, slowly, was turning its attention to the new statistic, OPS (on base plus slugging). OPS was the simple addition of on-base and slugging percentages. Crude as it was, it was a much better indicator than any other offensive statistic of the number of runs a team would score. Simply adding the two statistics together, however, implied they were of equal importance. If the goal was to raise a team’s OPS, an extra percentage point of on-base was as good as an extra percentage point of slugging.

Before his thought experiment Paul had felt uneasy with this crude assumption; now he saw that the assumption was absurd. An extra point of on-base percentage was clearly more valuable than an extra point of slugging percentage—but by how much? He proceeded to tinker with his own version of Bill James’s “Runs Created” formula. When he was finished, he had a model for predicting run production that was more accurate than any he knew of. In his model an extra point of on-base percentage was worth three times an extra point of slugging percentage.

Paul’s argument was radical even by sabermetric standards. Bill James and others had stressed the importance of on-base percentage, but even they didn’t think it was worth three times as much as slugging. Most offensive models assumed that an extra point of on-base percentage was worth, at most, one and a half times an extra point of slugging percentage. In major league baseball itself, where on-base percentage was not nearly so highly valued as it was by sabermetricians, Paul’s argument was practically heresy.

Paul walked across the hall from his office and laid out his argument to Billy Beane, who thought it was the best argument he had heard in a long time. Heresy was good: heresy meant opportunity. A player’s ability to get on base—especially when he got on base in unspectacular ways—tended to be dramatically underpriced in relation to other abilities. Never mind fielding skills and foot speed. The ability to get on base—to avoid making outs—was underpriced compared to the ability to hit with power. The one attribute most critical to the success of a baseball team was an attribute they could afford to buy. At that moment, what had been a far more than ordinary interest in a player’s ability to get on base became, for the Oakland A’s front office, an obsession.

To most of baseball Johnny Damon, on offense, was an extraordinarily valuable leadoff hitter with a gift for stealing bases. To Billy Beane and Paul DePodesta, Damon was a delightful human being, a pleasure to have around, but an easily replaceable offensive player. His on-base percentage in 2001 had been .324, or roughly 10 points below the league average. True, he stole some bases, but stealing bases involved taking a risk the Oakland front office did not trust even Johnny Damon to take. The math of the matter changed with the situation, but, broadly speaking, an attempted steal had to succeed about 70 percent of the time before it contributed positively to run totals.

The offense Damon had provided the 2001 Oakland A’s was fairly easy to replace; Damon’s defense was not. The question was how to measure what the Oakland A’s lost when Terrence Long, and not Johnny Damon, played center field. The short answer was that they couldn’t, not precisely. But they could get closer than most to an accurate answer—or thought that they could. Something had happened since Bill James first complained about the meaninglessness of fielding statistics. That something was new information, and a new way of thinking about an old problem. Oddly, the impulse to do this thinking had arisen on Wall Street.

In the early 1980s, the U.S. financial markets underwent an astonishing transformation. A combination of computing power and intellectual progress led to the creation of whole new markets in financial futures and options. Options and futures were really just fragments of stocks and bonds, but the fragments soon became so arcane and inexplicable that Wall Street created a single word to describe them all: “derivatives.” In one big way these new securities differed from traditional stocks and bonds: they had a certain, precisely quantifiable, value. It was impossible for anyone to say what a simple stock or bond should be worth. Their value was a matter of financial opinion; they were worth whatever the market said they were worth. But fragments of a stock or bond, when you glued them back together, must be worth exactly what the stock or bond was worth. If they were worth more or less than the original article, the market was said to be “inefficient,” and a trader could make a fortune trading the fragments against the original.

For the better part of a decade there were huge, virtually riskless profits to be made by people who figured this out. The sort of people who quickly grasped the math of the matter were not typical traders. They were highly trained mathematicians and statisticians and scientists who had abandoned whatever they were doing at Harvard or Stanford or MIT to make a killing on Wall Street. The fantastic sums of money hauled in by the sophisticated traders transformed the culture on Wall Street, and made quantitative analysis, as opposed to gut feel, the respectable way to go about making bets in the market. The chief economic consequence of the creation of derivative securities was to price risk more accurately, and distribute it more efficiently, than ever before in the long, risk-obsessed history of financial man. The chief social consequence was to hammer into the minds of a generation of extremely ambitious people a new connection between “inefficiency” and “opportunity,” and to reinforce an older one, between ““brains” and “money.”

Ken Mauriello and Jack Armbruster had been part of that