Author: Ratko V Tomic
Date: 15:13:32 07/29/00
Go up one level in this thread
>>This model is wrong and does not use the results of the games correctly to get >>the best estimate. > > The model can't be wrong. It does what it purports to do. > It does that well. The statistical model (memoryless random process) which ELO computation assumes to undelie the variablity in results is certainly not the accurate model for that variablity. The most accurate model would be the replica of the player itself. With chess programs one can make such exact model, not with humans. The statistical model for variability used in ELO is the simplest nontrivial statistical model. Of course, what you mean by "the model can't be wrong" is that one can apply correctly the inaccurate model in the sense of recognizing its incorrectness. That doesn't mean it reflects the process it models accurately or even well or better than any other model. The Uri's assertion is that the model ELO uses for mimicking variability in results is by no means accurate (corresponding to the actual results) and it isn't even the best one in present day and age. As he suggested, one could in principle write a program which could extract much more information from the game (e.g. via analysis and scoring of each ply) than ELO model does and be more accurate in predicting results than ELO. The ELO model uses about 1.58 bits of info for the entire game. The strength analyzer program Uri mentioned would use about 5 bits of info per ply, or hundreds of times more info per game about the process than the ELO does. Of course, ELO method is from the pre-computer era, devised to do best one can assuming: 1) evaluator need not know anything about the chess 2) evaluator need not use anything beyond the slide rule or log tables and pencil and paper to compute quickly the ratings and the predictions. If you drop either or both of these upfront restrictions (which are an arbitrary historical accident of what technology was available at the time) you can do much better in terms of predicting the outcomes from the previous games. A trivial example of such improved prediction for comp-comp play would be to run a simulation of the programs on a much faster computer than what they would play in a competition for which you're trying to predict an outcome. Such model is obviously better than ELO rating.
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