There is only one way to do this, and it is not going to be that accurate. You simply record the evaluation at each point in the game. And then record the game result. And then you repeat for lots of games. And then measure how well an evaluation of +XXX correlates with a game result of 1-0. And the variance will be huge. I watched a game between Rybka 3 and some hiarcs version on ICC a few nights ago where the operator said R3 had an evaluation of +2.3, and the game ended in a draw. Not the first nor last time that will happen. So clearly, Larry's numbers are the result of a curve-fit, with an immense error range. If you were to play Rybka against much weaker opponents, then +.3 might correlate almost 100% with winning chances, And you might find that -.2 correlates with 55% winning chances.ozziejoe wrote:george, this is a little off topic, but can you tell me the formula for converting rybka evalaution to win percentage, as you did in your post

That is the kind of evaluation we would all like to have. But we are a _long_ way (as in decades) away from being able to predict outcomes that precisely, when you see programs reach +8 and draw, or -5 and draw, and all the other improbable events that happen in comp vs comp games.
Most programmers would say, and believe, that +eval means their winning chances are above 50%. Otherwise the evaluation would be useless. But most of us stop at that point and don't try to extrapolate winning chances for specific eval values, because we know that the answer could be quite accurate when looked at over thousands of games, but for a single game, it would be little more than a guess.