Le jeudi 6 décembre 2007, Don Dailey a écrit :
>
> Lavergne Thomas wrote:
> > If some bot can be setup to play on kgs for enough time to get a solid
> > rank and then put on cgos to get an elo rating with the same
> > configuration we could find a formula to convert elo to kgs ranks.
> > For sure, this is not perfect but I think is good enought.
> >
> But most of us feel that you cannot do this with GO programs - you need
> humans. For instance you could take GnuGo, get a 19x19 rating and
> then play on 9x9 CGOS and use it as a reference point. However GnuGo
> was not designed to play 9x9 go. My own program Lazarus is terrible at
> 19x19 but pretty good at 9x9. It could probably give a low kyu player
> a really good game on 9x9 but it would be easily beat at 19x19 - so it's
> not a good way to standardize. I believe GnuGo is more balanced in
> this way - but it's probably a bad idea in general to figure it this way.
>
> Your idea is fine for 19x19 CGOS.
>
> - Don
I used 946 9x9 games of my gnugo bot on kgs:
On 9x9 it appears that gnugo _has_ the same rank as on 19x19.
So just giving standard gnugo a fixed kyu on cgos (lets say 6k like kgs)
should give rather good estimation.
/Alain
PS: Here are the histograms for my ranked bots on kgs, for those who wants
to do more subtle estimation and cannot wait monthes before their bot
gathered enough games on kgs :)
(these are old games before ranking system changes, so gnugo 3.7.9 and 10
were 13k kgs, they are now 6k)
I filtered out handicap games and unfinished, but did not check cheaters.
The tables are #occurence #opponent_rank(kgs_old_kyu)
For komi 7 and 7.5
gg99_rank/km7$ cat black_lose_agaisnt.csv
1 2
1 10
2 13
gg99_rank/km7$ cat black_wins_agaisnt.csv
1 10
1 12
2 13
gg99_rank/km7$ cat white_wins_agaisnt.csv
1 2
3 9
1 10
1 11
19 12
44 13
2 15
4 16
3 18
2 19
1 20
2 21
2 25
2 26
4 30
gg99_rank/km7$ cat white_lose_agaisnt.csv
1 3d
1 2
5 9
2 10
1 11
4 12
47 13
1 14
2 16
1 17
2 18
1 19
1 21
2 25
6 30
For komi 0 and 0.5 :
gg99_rank/km0$ cat black_wins_agaisnt.csv
1 5
1 6
13 7
14 8
16 9
11 10
34 11
50 12
2 14
3 17
1 18
1 24
1 30
gg99_rank/km0$ cat black_lose_agaisnt.csv
1 3
1 4
2 5
1 6
6 7
11 8
17 9
5 10
23 11
23 12
1 17
1 20
gg99_rank/km0$ cat white_wins_agaisnt.csv
28 12
211 13
5 25
1 30
gg99_rank/km0$ cat white_lose_agaisnt.csv
31 12
245 13
1 14
2 16
1 17
1 22
4 25
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