Moneyball by Lewis, Michael (mobile ebook reader txt) đź“•
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The central insight that led him both to turn minor league nobodies into successful big league closers and to refuse to pay them the many millions a year they demanded once they became free agents was that it was more efficient to create a closer than to buy one. Established closers were systematically overpriced, in large part because of the statistic by which closers were judged in the marketplace: “saves.” The very word made the guy who achieved them sound vitally important. But the situation typically described by the save—the bases empty in the ninth inning with the team leading—was clearly far less critical than a lot of other situations pitchers faced. The closer’s statistic did not have the power of language; it was just a number. You could take a slightly above average pitcher and drop him into the closer’s role, let him accumulate some gaudy number of saves, and then sell him off. You could, in essence, buy a stock, pump it up with false publicity, and sell it off for much more than you’d paid for it. Billy Beane had already done it twice, and assumed he could do so over and over again.
Jason Isringhausen’s departure wasn’t a loss to the Oakland A’s but a happy consequence of a money machine known as “Selling the Closer.” In return for losing Isringhausen to the St. Louis Cardinals, the A’s had received two new assets: the Cardinals’ first-round draft pick, along with a first-round compensation pick. The former they’d used to draft Benjamin Fritz, a pitcher they judged to have a brighter and cheaper future than Isringhausen; the latter, to acquire Jeremy Brown.
The Blue Ribbon Commission had asked the wrong question. The question wasn’t whether a baseball team could keep its stars even after they had finished with their six years of indentured servitude and became free agents. The question was: how did a baseball team find stars in the first place, and could it find new ones to replace the old ones it lost? How fungible were baseball players? The short answer was: a lot more fungible than the people who ran baseball teams believed.
Finding pitchers who could become successful closers wasn’t all that difficult. To fill the hole at the back of his bullpen Billy had traded to the Toronto Blue jays a minor league third baseman, Eric Hinske, for Billy Koch, another crude fireballer. He knew that Hinske was very good—he’d wind up being voted 2002 Rookie of the Year in the American League—but the Oakland A’s already had an even better third baseman, Eric Chavez. Plus, Billy knew that, barring some disaster, Koch, too, would gain a lot of value as an asset. Koch would get his saves and be perceived by other teams to be a much more critical piece of a successful team than he actually was, whereupon the A’s would trade him for something cheaper, younger, and possibly even better.
The loss of Johnny Damon, the A’s former center fielder, presented a different sort of problem. When Damon signed with Boston, the A’s took the Red Sox’s first-round pick (to select Nick Swisher) plus a compensation pick. But Damon left two glaring holes: on defense in center field, on offense in the leadoff spot. Of the two the offense was the easiest to understand, and dismiss. When fans watched Damon, they saw the sort of thrilling leadoff hitter that a team simply had to have if it wanted to be competitive. When the A’s front office watched Damon, they saw something else: an imperfect understanding of where runs come from.
Paul DePodesta had been hired by Billy Beane before the 1999 season, but well before that he had studied the question of why teams win. Not long after he’d graduated from Harvard, in the mid-nineties, he’d plugged the statistics of every baseball team from the twentieth century into an equation and tested which of them correlated most closely with winning percentage. He’d found only two, both offensive statistics, inextricably linked to baseball success: on-base percentage and slugging percentage. Everything else was far less important.
Not long after he arrived in Oakland, Paul asked himself a question: what was the relative importance of on-base and slugging percentage? His answer began with a thought experiment: if a team had an on-base percentage of 1.000 (referred to as “a thousand”)—that is, every hitter got on base—how many runs would it score?* An infinite number of runs, since the team would never make an out. If a team had a slugging percentage of 1.000—meaning, it gained a base for each hitter that came to the plate—how many runs would it score? That depended on how it was achieved, but it would typically be a lot less than an infinite number. A team might send four hitters to the plate in an inning, for instance. The first man hits a home run, the next three make outs. Four plate appearances have produced four total bases and thus a slugging percentage of 1.000 and yet have scored only one run in the inning.
These “percentages” are designed to drive anyone who thinks twice about them mad. It’s one thing to give 110 percent for the team, but it is another to get on base 1,000 percent of the time. On-base “percentage” is actually on-base “per thousand.” A batter who gets on base four out of ten times has an on-base “percentage” of four hundred (.400). Slugging “percentage” is even more mind-bending, as it is actually “per four thousand.” A perfect slugging “percentage”—achieved by hitting a home run every time—is four thousand: four bases for every plate appearance. But for practical purposes, on-base and slugging are assumed to be measured on identical scales. At any rate, the majority of big league players have on-base percentages between three hundred (.300) and four
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