Managing The Assets

Paul DePodesta, Assistant GM of the Oakland A's, talks about the transformation of the organization:

(Update: DePodesta has just been named the GM of the Los Angeles Dodgers.)

I was on a quest to find relevant relationships. Usually it wasn't as simple as "if X then Y." I was looking for probabilistic relationships. I christened the new model in the front office: "be the house." Every season we play 162 games. Individual players amass over 600 plate appearances. Starting pitchers face 1,000 hitters. We have plenty of sample size. I encouraged everyone to think of the house advantage in everything we did. We may not always be right but we'd be right a lot more often than we'd be wrong. In baseball, if you win about 60% of your games, you're probably in the playoffs.

One of the other problems is that the traditional metrics and stats used in baseball are muddied with so much noise that just didn't matter that I was having a tough time distilling all the information. I decided to throw it all out and start all over with no assumptions. I built a Markov model, or actuarial table, for the last five or ten years that recorded what had actually happened in the course of every major league baseball game.

From that research I was able to figure out that a man on first with nobody out is worth "X" runs and a man on second with two outs is worth "Y" runs. From there I was able to jump to understanding what it means to have someone who can hit a lot of doubles. What was the value of that event and others? I went a step further and asked who the people were who could add these value?enhancing skills to our team. Finally I was able to figure out what the cost of each of those activities was and what the margins were. This was process versus outcome. I just didn't believe the outcomes that the traditional stats were giving us.

Once the research was complete, debated and stress?tested (which took years) we had considerable new knowledge, and a lot of it was pretty startling. Now remember that we hadn't really invented anything. We had only discovered relationships that were already there. Fortunately for us, most of them were contrary to popular opinion. These discoveries ranged from broad philosophical ideas, such as the fact that 90% of the player population in major league baseball is replaceable by someone who makes less to the very minute detail, such as pitch counts or control of the strike zone. What I ended up doing was creating a whole new set of metrics around this objective core. When I was done we had stats but not in the traditional sense. It was an entirely new operating system. It wasn't an upgrade from Subjective 1.0 to Subjective 2.0. It was more like "Winning Baseball 1.0."

There is now a push to bring the same type of statistical analysis to football, basketball, soccer and hockey. Though it may prove useful in football, I don't really see it having the same type of impact on the latter three sports that Sabermetrics has had on baseball, simply due to the lower cyborg ratio inherent in those sports.


Comments are closed.