Moneyball (Movie Tie-In Edition) (Movie Tie-In Editions) (19 page)

There was hardly a play in baseball that, to be precisely valued, didn’t need to be adjusted for the players involved, or the ballpark in which it occurred. What AVM’s system really wanted to know was: in every event that occurs on a baseball field, how—and how much—should the players involved be held responsible, and therefore debited and credited? Answer the question and you could answer many others. For example: How many doubles does Albert Belle need to hit to make up for the fly balls he doesn’t catch?

How
to account for a player’s performances was obvious: runs. Runs were the money of baseball, the common denominator of everything that occurred on a field.
How much
each tiny event on a baseball field was worth was a more complicated issue. AVM dealt with it by collecting ten years of data from major league baseball games, of every ball that was put into play. Every event that followed a ball being put into play was compared by the system to what had typically happened during the previous ten years. “No matter what happens in a baseball game,” said Armbruster, “it has happened thousands of times before.” The performance of the players involved was always judged against the average.

A lot of this was no different from what Bill James and Dick Cramer had set out to do ten years earlier, when they created STATS Inc. The original contribution to new baseball knowledge of AVM Systems was how much more precisely it analyzed data, and how much more exactly it valued the performances of the players. Mauriello and Armbruster began by turning every major league diamond into a mathematical matrix of location points. Every point they marked with a number. They then reclassified every ball that was hit. There was no such thing in their record as a double; that was too sloppy. There were no such thing as pop flies, line drives, and grounders: finer distinctions needed to be made. A ball was hit with a certain velocity and trajectory to a certain grid on the field. In the AVM recording of a baseball game, a line drive double in the left-center gap became a ball hit with a certain force that landed on point #643.

The system then carved up what happened in every baseball play into countless tiny, meaningful fragments. Derivatives. “There are all sorts of things that happen in the context of a baseball play,” said Armbruster, “that just never got recorded.” A tiny example: after a single to right field a runner who had been on first base, seeing that Raul Mondesi is the right fielder, stops at second base instead of dashing to third. Runners seldom tried to go from first to third on balls hit to right field when Raul Mondesi was playing there. That was obviously worth something: what? Just as it never occurred to anyone on Wall Street to think about the value of pieces of a stock or a bond until there was a pile of money to be made from the exercise, it never occurred to anyone in the market for baseball players to assign values to the minute components of a baseball player’s performance—until baseball players became breathtakingly expensive.

Bill James’s work had been all about challenging the traditional understanding of the game, by questioning the meaning of its statistics. The financial experts at AVM took this idea even further, by recording the events that occurred on a baseball field without any reference whatsoever to the traditional statistics. It wasn’t just circumstantial statistics such as “RBIs” and “saves” that the AVM model ignored. It ignored all conventional baseball statistics. The system replaced the game seen by the ordinary fan with an abstraction. In AVM’s computers the game became a collection of derivatives, a parallel world in which baseball players could be evaluated more accurately than they were evaluated in the real world.

Paul DePodesta was an intern for the Cleveland Indians when he met the former Wall Street traders turned baseball analysts, making their first sales trip around Major League Baseball. He remembers his reaction to their presentation:
Oh my God.
“It opened my eyes for me,” said Paul. “The biggest thing that AVM does is extract the element of luck. Everyone in baseball knows how much luck is involved in the game but they all say, ‘The luck evens out.’ What AVM was saying is that it doesn’t. It’s not good enough to say, ‘Aw, it just evens out.’”

An insight born in the financial markets took root in the minds of a young man who would soon have the power to put it to use inside Major League Baseball. Not long after Billy Beane had hired Paul DePodesta, in 1998, Paul persuaded Billy to hire AVM Systems. “They were still interesting to me,” Paul said, “because they weren’t churning conventional statistics in unconventional ways, which is what everyone else does.” AVM Systems was a luxury only a rich team could afford but that only a poor team, desperate for any edge, would think to use. Billy and Paul used the AVM system for a couple of years and then, to save money, copied what AVM did. Once Paul finished replicating the parallel world of derivatives, he and Billy could begin to answer more accurately the question about Johnny Damon’s defense.

Every event on a baseball field Paul understood as having an “expected run value.” You don’t need to be able to calculate expected run values to understand them. Everything that happens on a baseball field alters, often very subtly, a team’s chances of scoring runs. Every event on a baseball field changes, often imperceptibly, the state of the game. For example, the value of having no runners on base with nobody out and no count on the batter is roughly .55 runs, because that is what a baseball team, on average, will score in that situation. If the batter smacks a double, he changes the “state” of the game: it’s now nobody out with a runner on second base. The expected run value of that new “state” is 1.1 runs. It follows that the contribution of a leadoff double to a team’s expected runs is .55 runs (1.1 minus .55). If the batter, instead of hitting a double, strikes out, he lowers the team’s expected run value to roughly .30 runs. The cost of making that out was therefore .25 runs—the difference between the value of the original state of the game and the state the batter left it in.

But those calculations really only scratch the surface of the problem. If you want to strip out the luck and get to a deeper understanding of the value of a player’s performance you have to pose the baseball equivalent of existential questions. For instance: what is a double? It really isn’t enough to say that a double is when a runner hits a ball and gets to second base without a fielder’s error. Anyone who has seen a baseball game knows that all doubles are not alike. There are doubles that should have been caught—just as there are balls that are hit that should have been doubles but were plucked from the air by preternaturally gifted fielders. There are lucky doubles and unlucky outs. To strip out the luck what you need, really, is something like a Platonic idea of a double.

A set of Platonic ideas is one of the gifts the Wall Street traders gave to Paul DePodesta. The precision of the AVM system, copied by Paul, enabled him to think about every event that occurred on a baseball field in a new and more satisfying way. Any ball hit anyplace on a baseball field had been hit just that way thousands of times before: the average of all those hits was the Platonic idea. Call it a line drive hit at
x
trajectory and
y
speed to point #968. From the ten years worth of data, you can see that there have been 8,642 practically identical hits. You can see that 92 percent of the time the hit went for a double, 4 percent for a single, and 4 percent it was caught. Suppose the average value of that event is .50 of a run.
No matter what actually happened
, the system credits the hitter with having generated .50 of a run, and the pitcher with having given up .50 of a run. If Johnny Damon happens to get one of his trademark jumps and makes a sprawling catch, he is credited with saving his team .50 of a run.

The beauty of the value of that hit (or catch) was that the game gave it to you; the game
told
you how valuable every event was, by telling you how valuable it had been, on average, over the past ten years. By listening to what the game told him about the value of events, Paul could take every ball hit between in the area broadly defined as center field and determine its “expected run value.”

Which brings us back to Johnny Damon. Over the 2001 season many hundreds of balls had been hit by opponents of the Oakland A’s in the vicinity typically covered by the center fielder. By totaling up the outcomes when Johnny Damon was in the field, and comparing them to the average, Paul was able to see how many runs Damon had saved the team. He was also able to estimate how many runs Damon’s likely replacement, Terrence Long, would cost the team. Some of this you could see with the naked eye, of course. You could see Johnny Damon break the instant the ball left the bat. You could see Terrence Long freeze, or even take off in the wrong direction, when the ball was in midflight. You didn’t really need Wall Street traders to tell you which one was the better center fielder. The system born on Wall Street simply helped Paul to put a price on the difference. There was no longer any need to guess. There was no need for gut instinct, or conventional fielding statistics. The total cost of having Terrence Long, rather than Johnny Damon, in center field was fifteen runs, or about a run every ten games.

Fifteen runs was not a trivial number. In the end, Paul concluded that Johnny Damon’s fielding was more important than Billy Beane believed—the first pamphlet Billy had read on the subject had said that fielding was “no more than 5%” of baseball—but not so much more that you wanted to pay Johnny Damon the $8 million a year his agent was asking for. And the truth was that you still couldn’t make perfectly definitive statements about fielding. “There was still no exact number,” Paul said, “because the system doesn’t measure where a defensive player started from. It doesn’t tell you how far a guy had to go to catch a ball.” What looked like superior defense might have been brilliant defensive positioning by the bench coach.

There was one other big glitch: these sorts of calculations could value only past performance. No matter how accurately you valued past performance, it was still an uncertain guide to future performance. Johnny Damon (or Terrence Long) might lose a step. Johnny Damon (or Terrence Long) might take to drink, or get divorced. Johnny Damon (or Terrence Long) might decide that he’d made enough money already and lose his middle-class enthusiasm for running down fly balls. In human behavior there was always uncertainty and risk. The goal of the Oakland front office was simply to minimize the risk. Their solution wasn’t perfect, it was just better than the hoary alternative, rendering decisions by gut feeling.

Of one thing they were certain: their system brought you a lot closer to the true value of a player’s performances than anything else like it. And it reinforced the Oakland A’s working theory that a guy’s hitting ability had a far greater effect than his fielding ability on a team’s performance. Albert Belle missed more fly balls than any other left fielder in baseball, but the system proved that he more than made up for it by swatting more doubles. Or as Paul put it, “The variance between the best and worst fielders on the outcome of a game is a lot smaller than the variance between the best hitters and the worst hitters.” The market as a whole failed to grasp this fact, and so placed higher prices than it should on defensive skills. Thus the practical answer to the question about Johnny Damon’s defense: it would probably cost more to replace than it was worth. Anyone who could play center field so well as Damon was either a lot worse offensively than Damon, or overpriced. The most efficient way to offset the loss of Johnny Damon’s defense was to add more offense.

The Blue Ribbon Panel Report
believed that a poor team could never survive the loss to free agency of its proven stars. But the business was more complicated than that. The departures of Johnny Damon and Jason Isringhausen, both proven stars, were not great blows to the Oakland A’s. The loss of Isringhausen was not really a loss at all but a piece of ruthless profiteering. Damon’s was a loss but nothing like the $32 million for four years the Red Sox had guaranteed him. If the Oakland A’s had lost just those two players Paul’s computer might have predicted that the team in 2002 would win as many games as they had in 2001. But they’d also lost Jason Giambi, and Jason Giambi was another matter. Giambi was maybe the worst defensive first baseman in the big leagues but he was a machine for creating runs, one of the most
efficient
offensive players in the game. Worse, Giambi was back in Oakland, playing for the other team.

Chapter Seven

GIAMBI’S HOLE

We’re going to run the organization from the top down. We’re controlling player personnel. That’s our job. I don’t apologize for that. There’s this belief that a baseball team starts with the manager first. It doesn’t.

—Billy Beane, quoted in the
Boston Herald
, January 16, 2003

T
HE OAKLAND A’S
clubhouse was famously the cheapest and least charming real estate in professional baseball and the video room was the meanest corner of it. Off-limits to reporters, just a few yards down the hall from the showers, the video room was where the players came to hide from newspaper reporters, and to study themselves. One wall was stacked with old tapes of A’s games, the other with decrepit video equipment. Stained Formica desks, a pair of old video screens on each one, squatted on either end of the room. The only decoration was a plastic map of the United States—because occasionally the players wanted to see which states they’d fly over on the next road trip—and two pieces of a bat split against one of the Formica desks by former A’s outfielder Matt Stairs. About six baseball players could fit inside the room at once, and often did.

Between Matt Stairs’s broken bat and the U.S. map usually sat a young man named Dan Feinstein—Feiny, everyone called him. Twenty minutes before game time all that was left of the players in the video room was Miguel Tejada’s Fig Newton wrappers. Feiny spotted them and shook his head. The A’s shortstop was one of those people who had to be told to clean up his own mess, and Feiny was one of those people who wouldn’t hesitate to do it.