www.goratings.org is online with ratings of top professional

[emeraldemon]

Thanks again for a great website. I was wondering, does the "Elo" reported here work the same way as traditional Elo for estimating a winner? For example Iyama Yuta and Yamashita Keigo are going to play the 4th Gosei title match in a few days. Currently they have ratings of 2472 and 2368 respectively. Using the usual formula that translates to a 64.5% chance for Iyama Yuta to win. Do you think that's a good estimate, or do these numbers represent something different?

[Uberdude]

According to igokisen Iyama leads Yamashita 28-18 in head-to-head, which is a 61% win rate. This is admittedly over all time so would likely include a young Iyama when he wasn't as strong as he is now losing against an already mature Yamashita so might understate his current superiority. Looking in go4go since 2012, it is 25-11 which is 69% win. That elo one is about in the middle of those .

[John Fairbairn]

According to igokisen Iyama leads Yamashita 28-18 in head-to-head, which is a 61% win rate. This is admittedly over all time so would likely include a young Iyama when he wasn't as strong as he is now losing against an already mature Yamashita so might understate his current superiority. Looking in go4go since 2012, it is 25-11 which is 69% win. That elo one is about in the middle of those .

There are only three games before 2010 and Iyama won 2-1 there, so the uppityness of youth applies even in Japan.

GoGoD has a score 32-19 to Iyama over 51 games. They have played 15 times so far in 2015 alone, and Iyama leads 10-5.

One possibly interesting factoid: Iyama leads only 14-10 when taking White but 18-9 as Black. As Black Iyama has varied his openings to an immense degree. Maybe that is what is troubling Yamashita.

[oren]

Takao just won the playoff for Meijin challenger so we won't see yet another Yamashita/Iyama series for a bit.

[Rémi]

Thanks again for a great website. I was wondering, does the "Elo" reported here work the same way as traditional Elo for estimating a winner? For example Iyama Yuta and Yamashita Keigo are going to play the 4th Gosei title match in a few days. Currently they have ratings of 2472 and 2368 respectively. Using the usual formula that translates to a 64.5% chance for Iyama Yuta to win. Do you think that's a good estimate, or do these numbers represent something different?

Yes, they work the same as "traditional" Elo.

[by78]

Thanks again for a great website. I was wondering, does the "Elo" reported here work the same way as traditional Elo for estimating a winner? For example Iyama Yuta and Yamashita Keigo are going to play the 4th Gosei title match in a few days. Currently they have ratings of 2472 and 2368 respectively. Using the usual formula that translates to a 64.5% chance for Iyama Yuta to win. Do you think that's a good estimate, or do these numbers represent something different?

Yes, they work the same as "traditional" Elo.

Could you explain why Iyama is currently ranked #4, and excluding AlphaGo, #3 human player in the world?

Iyama's ranking seems unrealistically high. Seeing that Japanese players rarely compete internationally, how are Japanese pros' games weighed?

[Uberdude]

I found it amusing that Thomas Debarre, the 6d amateur from France, is ranked about Rin Kaiho 9p from Japan/Taiwan, who although getting rather old could surely easily beat Thomas.

[pookpooi]

Thanks again for a great website. I was wondering, does the "Elo" reported here work the same way as traditional Elo for estimating a winner? For example Iyama Yuta and Yamashita Keigo are going to play the 4th Gosei title match in a few days. Currently they have ratings of 2472 and 2368 respectively. Using the usual formula that translates to a 64.5% chance for Iyama Yuta to win. Do you think that's a good estimate, or do these numbers represent something different?

Yes, they work the same as "traditional" Elo.

Could you explain why Iyama is currently ranked #4, and excluding AlphaGo, #3 human player in the world?

Iyama's ranking seems unrealistically high. Seeing that Japanese players rarely compete internationally, how are Japanese pros' games weighed?

There's no concept of nationality in algorithm, if Japan pro has like 60% of game database and international game has 40% the international game is treated as a minority here.

Edit: For algorithm, Japan game means group A that usually love to play among each other, and international game means group B that usually play among each other, and if group A is larger algorithm doesn't see why it has to give more weight to group B.

[Kirby]

There's no concept of nationality in algorithm, if Japan pro has like 60% of game database and international game has 40% the international game is treated as a minority here.

Edit: For algorithm, Japan game means group A that usually love to play among each other, and international game means group B that usually play among each other, and if group A is larger algorithm doesn't see why it has to give more weight to group B.

Right. Algorithm doesn't bias based on nationality, but depends on the games we give it. So rating will inherently reflect the data the algorithm is given.

[hyperpape]

Here's a simple idealized story. There's enough international play for the algorithm to judge that the best Chinese and Korean go players are much better than Yamashita, Kono, Takao, Yuki, Yu, Kyo, and other Japanese players. The algorithm also judges that Iyama is much better than those players. Specifically, it estimates that he'll beat them something like 75% of the time. It estimates that Park Junghwan will beat them slightly more, Lee Sedol slightly less. Based on those estimates, it says Park is stronger than Iyama, Lee is a little weaker.

Are these estimates accurate? Absent a lot of head to head games between Iyama and players at his level, it is hard to be confident. I can think of three ways they could fail to be accurate:

1. Those Japanese players that Iyama is being compared to are themselves overrated. That would be visible in the data: the model would predict that they win more games against Korean/Chinese players than they do.
2. The assumptions about how strength is distributed are bad--maybe if you look at the top five players in the world, the Chinese/Koreans beat the 70th-80th rated players more than Iyama does, but for some reason, the math works out that he has a high rating[0]. This would *also* be visible in the data.
3. Iyama's performance against Japanese players does not predict his performance against Korean/Chinese players. Maybe he's gotten into the heads of the other Japanese players, but can't translate that into wins against international competition. In that case, playing against international competition would harm his rating (there's a sense in which the model used predicts that if two players play a series of games, they should not expect their ratings to change). This possibility can't be judged based on the game records we have.

In Remi's original paper on KGS, he evaluated his models predictions with regard to the KGS users. Eventually someone should do the same for professionals.

[0] I don't even know if the math for this works out. I suspect it could, but who knows?

[Elom]

If Goratings had sufficient Data...

Is it the elo rating system, etc. it assumes that if a player is rated A points higher and has X excess winning percentage, and if rated B points higher should have, for demonstrative sake, 2X excess win percentage. It could be overestimating the difference in skill level between Iyama and the rest of the Japanese pros by believing "that for him to maintain such a high winning percentage against all the other Japanese professionals, he must be X points above them!". The Japanese pro's are generally underrated, Iyama is overatted as there is little international calibration?

[pookpooi]

Do we judge that Japanese pro are weaker than Chinese/Korean based on few games? I say 'few games' from algorithm's point of view because if it has enough game, with enough result, it must be shown in the ranking, and Iyama Yuta wouldn't be rank so high, would he?
Since everyone claim that Japanese pro rarely play a game in international match, then how could we be so sure that they're being overestimated? Based on those 'rarely' played games? that maybe enough to convince our sense that Japanese pro is weaker, but unless more game is played right now it's not enough to convince the ranking's algorithm.

#My reply is not an answer to Hyperpape's question (which is why I don't quote him) He ask a different question

[Solomon]

Do we judge that Japanese pro are weaker than Chinese/Korean based on few games? I say 'few games' from algorithm's point of view because if it has enough game, with enough result, it must be shown in the ranking, and Iyama Yuta wouldn't be rank so high, would he?
Since everyone claim that Japanese pro rarely play a game in international match, then how could we be so sure that they're being overestimated? Based on those 'rarely' played games? that maybe enough to convince our sense that Japanese pro is weaker, but unless more game is played right now it's not enough to convince the ranking's algorithm.

#My reply is not an answer to Hyperpape's question (which is why I don't quote him) He ask a different question

The algorithm does not look at Iyama Yuta's game records to assess his strength; Chinese and Korean pros do.

[Kirby]

There's enough international play for the algorithm to judge that the best Chinese and Korean go players are much better than Yamashita, Kono, Takao, Yuki, Yu, Kyo, and other Japanese players. The algorithm also judges that Iyama is much better than those players.

It is difficult to extrapolate assumptions about the general population from a small sample when you are looking for an absolute ranking for the general population. For example, I was the valedictorian of my small high school of 82 people, and also had the highest standardized test scores. Naively, you could say that I was in the top 1~2%, academically. With no other data about the distribution of academic ability among students from other schools, I could try to say I was in the top 1~2% of students in the world. But that is bull****. Once I entered an environment having a greater number of students, competition grew, and I realized that there were many other skilled students. Among the students at my university, if I had to guess, I might guess that I were in the top 30% academically, but there's no way I could claim that I were in the top 1~2%. Competition was much more severe. And this is just talking about percentages. Even if my school were representative of the general population, being in the #1 position at my school in no way suggests that I will be #1 in a larger population.

So however you want to frame the mathematics, it's always going to be dependent on the dataset. You take a handful of Iyama Yuta's games from a smaller population, try to extrapolate it onto a larger population... well, you're just shooting in the dark. Maybe you're right, and maybe you're wrong.

The only way to know for sure is to get real data from the larger population (i.e. get everyone to play games with one another worldwide, since that's what this list represents).

[pookpooi]

Do we judge that Japanese pro are weaker than Chinese/Korean based on few games? I say 'few games' from algorithm's point of view because if it has enough game, with enough result, it must be shown in the ranking, and Iyama Yuta wouldn't be rank so high, would he?
Since everyone claim that Japanese pro rarely play a game in international match, then how could we be so sure that they're being overestimated? Based on those 'rarely' played games? that maybe enough to convince our sense that Japanese pro is weaker, but unless more game is played right now it's not enough to convince the ranking's algorithm.

#My reply is not an answer to Hyperpape's question (which is why I don't quote him) He ask a different question

The algorithm does not look at Iyama Yuta's game records to assess his strength; Chinese and Korean pros do.

Again, whatever Chinese/Korean pro might say about Iyama Yuta, from algorithm's point of view we don't know for sure unless more data are inserted. They may be right though. But the problem is, they're able to give a better prediction of match's result by using different set of mind to evaluate each pro's strength by looking at the game record while Remi use only game result with single algorithm (and still give the best result among other rating method). This is why it'll be fun if we evaluate pro by have AlphaGo look at the game and give winrate in each move (given that AlphaGo is much stronger than it is right now and never lose to human again)