Thank you for your clarifications.
I implemented the rating algorithm and used it for random processes. In this way, I got that the standard deviation at 2000 is 44. Then I used this to obtain the empirical expected winrate by convolution, and I saw that the effect is there but it is indeed negligible.
The problem of systematically wrongly rated players is mostly important for the tails of the empirical winrate functions. But the tails are fixed by the Bradley-Terry formula, so now I see that they are not that important in fixing parameter a.
It would still be interesting to see how well Bradley-Terry captures empirical tails, and for that filtering would be important. I agree with your approach for filtering.
ps. Implementing the rating algorithm, I could now plot the average evolution of rating of a player playing against 2000 Gor rated players and winning 70%. This gives an indication of the needed number of A-rated tournament games against similarly rated opponents for your rating to converge.
