Author: Andrew Wagner
Date: 05:26:08 04/05/04
I'm taking this intro to calc-based statistics class this semester, and it got
me thinking. We tend to generate a lot of statistics during a game. In fact, we
could generate a LOT more. But we do very simple things with these statistics.
For example, we might add some bell or whistle to the search, notice that it
doesn't show any obvious increase in NPS or move ordering, or whatever we were
hoping for, and ditch it. That's an extremely simple analysis of the statistics.
I'm wondering if anybody has done any more advanced statistical work in figuring
out what factors are important to winning a game. Here are two examples I
thought of:
1.) Using advanced statistics to calculate better values to be used in
static eval. What if we took a ton of super-gm games, and generated a slew of
statistics about each game like number of moves white has an isolated pawn on,
or number of moves black keeps his king-knight, and so on. Then do a
multi-variate analysis to determine whether any of those things have an effect
on the probability that white or black will win. Unfortunately, this math is a
little above me at this point, and maybe someone else has tried it, I don't
know. It's an interesting idea to think about, though.
2.) Some quantitative analysis of the speed vs. knowledge question. We have
the speed number readily available, in the form of NPS. If we could find some
way to quantify the amount of knowledge we build into an engine, we could easily
find some kind of ideal balance. Think about it: this kind of thing is done all
the time in many fields. Mathematically, this kind of optimization should not be
all that hard, if we can come up with good numbers to use.
So, what do you think? Anybody heard of such a project before? Do you all
think it would work? I'd be interested in any thoughts you have. Andrew
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