H.G.Muller:
| Chess as we play it is a game of chance...

The main point is that such a statistic analysis is only valid with respect to a certain group of games, as long as as the players stick to a similar strategy. The situation is like with pseudo-random numbers, where in one case one discovered that if the successive numbers generated were plotted in triples, they fell into a series of sloped planes. Such a thing can be exploited. So there results a circle of making better pseudo-random generators and methods to detect flaws, without ever resulting in true random numbers. A similar situation results in cryptography.

In chess, the best strategy is trying to beat whatever underlying statistical theory the opponent is playing against. When playing against computer programs this is not so difficult, because one tries to figure what material exchanges the opposing program favors and shuns, and then tries playing into situations where that is not valid. Now, this requires that the human player gets the chance of fiddling around with the program interactively for some time in order to discover such flaws - learning this through tournament practice is a slow process - plus a way to beat the computers superior combinatorial skills if the latter is allowed to do a deeper search by brute force.

| Anyway, you cannot know what Kaufman thinks or doesn't.

His stuff looks like all the other chess theory I have seen, only that he uses a statistic analysis as an input, attempting to fine-tune it. By contrast, you are the only guy I have seen that thinks of it as a method to predict the average outcome of games. You might benefit from asking him, or others, about their theories - this is how it looks to me.

You might still do values and percentages and display them as your analysis of past games in a certain category, but there is gap in the reasoning claiming this will be true as a prediction for general games.