SOCIOL 155BW Chapter Notes - Chapter 1: Baseball Statistics, Adam Dunn, Runs Created
arch 11, 2004
Baseball Prospectus Basics
The Science of Forecasting
by Nate Silver
Call yourself a forecaster and you're sure to get some dirty
looks. It's a cultural tradition, at least in the parts of our
country that has seasons, to criticize the accuracy of a
weather forecast (
you call
this
partly cloudy, Mabel?
).
Political pundits--you know, the guys in the bowties--are
ranked somewhere between child molester and petty thief on
the social hierarchy. The stock market analysts that were the
toast of the town just a couple of years ago are now seen as
charlatans at best, criminals at worst.
My name is Nate, and I am a forecaster. I forecast how
baseball players are going to perform. And I pretty much get
the worst of it. Tell somebody that their childhood hero is
going to hit .220 next year, or that the dude they just traded
away from their fantasy team is due for a breakout, and
you're liable to get called all kinds of names. A bad
prediction will inevitably be thrown in your face, (see also:
Pena, Wily Mo) while a good one will be taken as self-
evident, or worse still, lucky.
The truth is, though, that those of us who make it our
business to forecast the performance of baseball players
have it pretty easy. For one thing, we've got an awesome set
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of data to work with; baseball statistics are almost as old as
the game itself, and the records, for the most part, are
remarkably accurate and complete. For another, it's easy to
test our predictions against real, tangible results. If we tell
you that Adam Dunn is going to have a huge season, and
instead he's been demoted to Chattanooga after starting the
year 2-for-53, the prediction is right there for everyone to see
in all its manifest idiocy. Not so in many other fields, where
the outcomes themselves are more subject to interpretation.
In fact, baseball forecasters are a pretty spoiled lot. We
don't have to deal with an intrinsically chaotic system, like
weather forecasters do, or with the whimsy of politics or
psychology. One of the unavoidable truths about being
spoiled is that it's going to make you lazy, and for years,
baseball forecasters were a lazy bunch. It's possible to come
up with a pretty good forecast simply by looking at how a
player has performed in the past three seasons, adjusting it
upward or downward slightly for his age, and maybe
applying a park effect. As a player has performed in the past,
so he will perform in the future; it's sunny today, and so it will
be sunny tomorrow.
* * *
Let's redirect our abstraction for moment and consider what
a baseball forecaster has to work with. Data are our fuel;
what do these data look like? Well, something like this:
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What we've got here is the performance of four baseball
players over the course of their careers. We've got their age
running from left to right, and their Value--that could mean
OPS, runs created, Equivalent Average, whatever you care
for--running from top to bottom. What we've also got is a
bloody mess. The lines criss and cross seemingly at
random; the performance of an individual player varies wildly
from year to year--this is what the data look like. If you squint
hard enough, you might notice that the players tend to do
better in the middle of their careers than at the beginning or
the end, but even that is easy to lose in this swamp of
randomness.
And then some smart person with an MBA came along and
invented the Average.
Take the average value of the four players in our chart, and
you come up with a perfectly well-behaved curve that
conforms with more or less all of the usual assumptions
about the progress that a major league baseball player is
likely to experience over the course of his career. He starts
out slowly upon his debut, improves rapidly through his early
20s, reaches his peak at age 26 or 27, and then begins his
decline, which is slow at first but soon becomes more rapid.
It all looks nice and orderly, and it is curves like this that
many forecasting systems are predicated upon. We expect a
player's productivity to decrease, say, by 3% between age
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