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[Adam Hair]

The following is a graph of the CCRL 40/40 data, prepared exactly how Edmund did. However, I have recomputed the ratings to take into account the eloAdvantage and eloDraw as computed from these games. I left prior equal to 2.



The equation for the logistic model seen in the graph is:

White Score = 100/(1+10^(-1.028*(X+32.976)/400))

where eloAdvantage = 32.976. Also, R²=0.972 for this model.

I guess if the prior was adjusted down, the 2.8% compression might completely go away. However, I believe that this shows the discrepancy between the the CCRL Elo ratings and the Bayeselo model is due to the fact that the default values for eloAdvantage and eloDraw are used to compute the ratings. If the computed values are used (which act like location and scale parameters for White Score), the resulting logistics model matches the data quite well (as it should).
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