>
> I recall reading about an Elo system that had better adaptation to players
> whose
> rating changes. It was called Glicko-2. Here is a link:
> http://en.wikipedia.org/wiki/Glicko_rating_system
>

This would be a very poor rating system for CGOS.   He basically recommends
giving much higher K factors than the USCF rating system uses.   This might
work better for humans who can vary in strength signficantly from one month
to the next but not for bots that usually have the same strength.

In general, the problem with incremental ratings is that they will never
converge on the true strength of a player.  And if you use a higher K
constant, the drift will be even more pronounced.

- Don






2009/4/20 <[email protected]>

> >At the moment, Pebbles is creating a huge drift. Brian - CGOS requires us
> to use
> >new names on the server each time we change our bots. It computes the
> strength
> >using all games (heavilly biased with the results of the first 100 games)
>
> This is basically my first working version. Its rating dropped from 1500 to
> 800
> while I worked out time control, position repetition, crash bugs and so on.
> Pebbles is basically just getting those points back.
>
> The solution to drift is to keep Fatman-1 running.
>
> I recall reading about an Elo system that had better adaptation to players
> whose
> rating changes. It was called Glicko-2. Here is a link:
> http://en.wikipedia.org/wiki/Glicko_rating_system
>
>
> >Pebbles is probably closer to 2000 ELO than 1000 ELO based on the games
> against my bots.
>
> Closer, yes, but I don't think it could be that high, as Pebbles has a
> significant minus
> in recent games against bots rated over 1800.
>
> Its Bayes Elo is 1519, which also does not suggest that it is headed for
> 2000.
>
> Also, it searches only 20K light (mostly) playouts per turn. (Old
> computer.)
>
>
> >I did spot a bug in hb797-50k in its first resignation to Pebbles. My
> count was hb797-50k +1.5
>
> Happy to help. :-)
>
>
> Brian
>
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