The graph fits like a movie. Impreessive wild sides of IGS.
1 Like
In that case start new at 15k and climb slowly until you reach some 50/50 wins/losses.
I don’t know exactly how IGS updates its rank, but as my example above, even after tens of thousands of games, the fluctuation can still be above 2 rank for some reason. Most players at the DDK rank who play a lot has this kind of graph (this player has more than 20000 games)
Even sdk player can have wild fluctuation (this player has more than 4000 games)
Only when players reach high sdk near dan level above, they will be stablized.
I think it might be related to the score differences in each rank gap jump significantly around dan ranks.
3 Likes
Yes, every 5 games the rank can be updated by several stones, for instance I looked at an account which was registered as “7k?”, and after 5 losses it became “10k?”. So once the rank is firm (without “?”), it becomes quite reliable.
Having fluctuations by 2-3 stones is absolutely normal, sometimes we put more effort into the game and sometimes less. Here is my graph on OGS:
A player on OGS has over 27000 games, but the OGS page only shows the last 5000:
Looks like a Brownian motion.
1 Like
I think IGS only shows the past 6 months at most, so could be just hundreds of games in their graph, and still fluctuates a lot in short term (like go up and down week to week). And after the initial phase, it’s updating daily instead of based on a number of batches.
When I was a beginner 5-6 years ago, a “17k?” guy on IGS beat me by 231 points. This guy is still sandbagging, every few games he kills large groups and then resigns. After more than 3000 games his rank is still “17k?”, I don’t understand how that’s possible to still have a provisional rank.
What a boring life.
1 Like
This would only be true if the there would be no fixed or meaningfully recognizable points on an infinite/endless ranking scale.
In Go, since perfect play is a fixed, few stones distance from current bots, if you specify ranks as X stones to perfect play (like in Asia, I sometimes see “X stones to pro level”) you can have ranks that are “real” in some sense.
Shouldn’t it work similarly when counting form “total beginners”?
The fact that beginners have different ranks on different servers doesn’t help…
I think beginner level is too inconsistent for this, not an accurate point like perfect play. Beginner level may even drift in time (with cultural changes in decades, etc).
1 Like
I’d say “perfect play” also drifts with time. It’s just that we are so close from the first instances of perfect play that it’s hard to tell.
I don’t understand this. If it could drift even by 0.01 points, if was not perfect to begin with. 
1 Like
True, but perfect play cannot actually be determined so it cannot be used to define ranks.
We could say “X stones from Katago” though.
On the other side, I agree “beginner” is too inconsistent but we could use “random play” as a basis
1 Like
Do we have some conversion of OGS ranks to X stones from a particular Katago bot? Does anybody have the data?
1 Like
I mean for a specific instance of katago one could do something like
to filter games where White lost and the handicap was >0, with the assumption that Katago will be white. One might have to grab them page by page though I’m not sure.
On the other hand…
One could ask here for someone to do this with the big data download
Yes, you can substract your rank from Katago’s rank.
This oc implies a bot that mostly plays handi games and a rating system which correctly handles those games.
IIRC you don’t need an actual perfect player to be able to estimate your distance to it. This is because as strength increases, so does consistency as well. So you cannot directly measure the strength of a player (only compare him to others), but can still get an idea of the consistency of his play.
I can’t prove it ofcourse, but I don’t think a perfect player can give KataGo running at 10 million playouts/move 9 stones handicap. My gut feeling is that the proper handicap would be close to 2 stones.
2 Likes
I’d be happy to help, but I don’t understand what I should do. 
Sorry, I may be dumb but I still don’t get how you would estimate your distance from a perfect player without knowing what perfect play is. How can you evaluate someone being “two stones from perfect play” ?
I agree with @gennan that you could go by a gut feeling that KataGo is somewhere around 2 stones from perfect play, but that’s just a guess.
1 Like
By measuring consistency (instead of strength) and its changes.
For example, you can compare how consistent is the play of 1d, 3d and 5d players, and how fast that consistency changes in this range. If it changes, say, 10% between 1d and 5d, this means more distance to perfect play than if it changes 20% (just random numbers for example).
1 Like
How do you define “consistency” here ? How is it measured ?