Moneyball by Lewis, Michael (mobile ebook reader txt) 📕

generation. Ken analyzed the value of derivative securities, and Jack traded them, for one of the more profitable Chicago trading firms. Their firm priced financial risk as finely as it had ever been priced. “In the late 1980s Kenny started looking at taking the same approach to Major League baseball players,” said Armbruster. “Looking at the places where the stats don’t tell the whole truth—or even lie about the situation.” Mauriello and Armbruster’s goal was to value the events that occurred on a baseball field more accurately than they ever had been valued. In 1994, they stopped analyzing derivatives and formed a company to analyze baseball players, called AVM Systems.

Ken Mauriello had seen a connection between the new complex financial markets and baseball: “the inefficiency caused by sloppy data.” As Bill James had shown, baseball data conflated luck and skill, and simply ignored a lot of what happened during a baseball game. With two outs and a runner on second base a pitcher makes a great pitch: the batter hits a bloop into left field that would have been caught had the left fielder not been Albert Belle. The shrewd runner at second base, knowing that Albert Belle is slow not just to the ball but also to the plate, beats the throw home. In the record books the batter was credited with having succeeded, the pitcher with having failed, and the left fielder and the runner with having been present on the scene. This was a grotesque failure of justice. The pitcher and runner deserved to have their accounts credited, the batter and left fielder to have theirs debited (the former should have popped out; the latter somehow had avoided committing an “error” and at the same time put runs on the board for the other team).

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