Carlsen withdrawal after loss to Niemann

[lkaufman]

Well, part of this thread has been about who we should believe.

I think you mix up something. It is not Carlsen vs. Niemann, Carlsen is not the only one who speaks out suspicions, also other GM's and different AI cheat detection systems are on the trigger. The question whether Hans is cheating or not has nothing to do with Carlsens credibility. so it is completely useless to question his credibility because he said "how" at the wrong time.

Also, there is no question that Carlsen really believes that Niemann is cheating. He may be mistaken about this, but it takes a flat earther to believe that Carlsen is lying.

Just because Carlsen is about 200 rating points higher does NOT mean GM Hans cheated, there are several other upsets where the difference in ratings are much higher and the higher rated player do NOT accuse the lower of cheating. Here are some latest Upsets. These two lower rated indian players were hibernating during COVI-19 and only studying chess the same as GM Hans, Gukesh, GM Pragg, and GM Arjun ==>
https://chessbase.in/news/1st-Bikaner-G ... d-1-report

[Event "1st Bikaner Open International Grandmast"]
[Site "Banquet Hall of Ashirwad Bhawa"]
[Date "2022.10.01"]
[Round "1.14"]
[White "LAXMAN, R.R.."]
[Black "SWAYHAM, P DAS."]
[Result "0-1"]
[ECO "A06"]
[WhiteElo "2378"]
[BlackElo "1902"]
[Annotator "Shahid"]
[PlyCount "106"]
[EventDate "2022.10.01"]
[EventRounds "10"]
[EventCountry "IND"]

1. d4 {1} d5 {9} 2. Nf3 {5} Nf6 {8} 3. c4 {4} c6 {6} 4. e3 {5} e6 {25} 5. b3 {6
} Nbd7 {11} 6. Bd3 {6} Bd6 {54} 7. Bb2 {10} b6 {91} 8. O-O {9} Bb7 {29} 9. Nc3
{49} O-O {17} 10. Qe2 {171} Re8 {83} 11. Rfd1 {37} Rc8 {148} 12. Rac1 {34} Qe7
{63} 13. cxd5 {66} Nxd5 {208} 14. Ne4 {571} Bb8 {189} 15. Ne5 {198} Nf8 {321}
16. Qh5 {315} f6 {155} 17. Nc4 {66} Nb4 {1261} 18. Bb1 {118} Red8 {382} 19. a3
{292} Na6 {171} 20. b4 {24} Rd5 {415} 21. Qe2 {137} Rcd8 {83} 22. Ncd2 {32} Nc7
{104} 23. Nf3 {231} R5d7 {295} 24. Re1 {197} Nd5 {225} 25. Qc2 {80} Qf7 {118}
26. Ng3 {28} Rc8 {92} 27. Ba2 {90} Kh8 {178} 28. Qe2 {399} Qe7 {50} 29. Ne4 {21
} Qf7 {54} 30. Bb1 {151} Qe7 {22} 31. g3 {22} Kg8 {77} 32. Ned2 {8} Qd8 {193}
33. Ba2 {125} Kh8 {15} 34. e4 {7} Ne7 {75} 35. Nf1 {23} Neg6 {69} 36. h4 {9}
Qe8 {109} 37. Ne3 {80} c5 {123} 38. d5 exd5 {136} 39. Nxd5 {41} (39. exd5)
39... Ne5 {75} 40. bxc5 {125} Rxc5 {66} 41. Rxc5 {2} bxc5 {4} 42. Bxe5 {45}
Bxe5 {10} 43. Qc4 {38} Ne6 {371} 44. Nxe5 {92} fxe5 {4} 45. Rb1 {50} h6 {87}
46. a4 {58} Bc6 {163} 47. a5 {408} Rd8 {8} 48. Qa6 {515} Qd7 {46} 49. Ne3 $2 {
116} (49. Ne7 Bxe4 (49... Qxe7 50. Qxc6 Nd4 51. Qb7) 50. Re1 Nd4 51. Rxe4 Qxe7
52. Kg2) 49... Nd4 $19 {50} 50. Bd5 {5} Qh3 {43} 51. Ng2 {39} Bxd5 {49} 52. Qd3
{29} Bc4 {12} 53. Qe3 {5} Ne2+ {4} 0-1


[Event "1st Bikaner Open International Grandmast"]
[Site "Banquet Hall of Ashirwad Bhawa"]
[Date "2022.10.01"]
[Round "1.15"]
[White "SINGH, PRITAM."]
[Black "NGUYEN, DUC HOA."]
[Result "1-0"]
[ECO "D80"]
[WhiteElo "1899"]
[BlackElo "2360"]
[Annotator "Shahid"]
[PlyCount "43"]
[EventDate "2022.10.01"]
[EventRounds "10"]
[EventCountry "IND"]

1. d4 {1} Nf6 2. c4 g6 3. Nc3 d5 4. Bg5 Ne4 5. Nxe4 dxe4 6. e3 f6 7. Bh4 Nc6 8.
a3 h5 9. h3 e5 10. dxe5 {295} Qxd1+ {32} 11. Rxd1 {48} g5 {9} 12. Bg3 {34} h4 {
15} 13. Bh2 {26} Nxe5 {31} 14. Bxe5 {146} fxe5 {6} 15. Ne2 {178} Rh6 {228} 16.
Nc3 {56} Bf5 {27} 17. Be2 {140} Rb6 $4 {278} (17... Rd8 18. O-O (18. Nd5 c6)
18... Rxd1 19. Rxd1 Rd6) 18. Nd5 $18 {768} Bd6 {1183} (18... Rc6 19. Bh5+ Kd8
20. Nb6+ $18) 19. c5 {247} Bxc5 {18} 20. Nxc7+ {10} Kf8 {30} 21. Nxa8 {75} Rxb2
{5} 22. Bg4 {91} 1-0

As far as I know, no one suspects Gukesh, Pragg, or Arjun of cheating OTB because none of them has been accused of cheating online. While it is true that cheating online does not prove cheating OTB, I think that the converse does apply, namely that NOT cheating online is pretty much proof of NOT cheating OTB, since it is so much easier to cheat undetected online than OTB.

[dkappe]

Brazilian Youtuber, owner of the biggest chess channel in Brazil on YouTube and who is also a programmer, seems to have managed to prove that Hans Nielman has cheated since 2018.

Video in english:


His YouTube channel (Xadrez Brasil, only portuguese): https://youtube.com/c/XadrezBrasil

It does look very suspicious that he seemed to maintain a fairly steady accuracy rate from 2018 on, but I find it strange that his rating took so long to climb from 2300 to 2700 if he was playing at the same level all of that time. Hard to reconcile those two facts.

One assumption here is that centipawn loss (CPL) is strongly correlated with rating. I tried to find any publications on this, and the only thing I found was here: https://kwojcicki.github.io/blog/CHESS-BLUNDERS

Their conclusion was that rating wasn’t that strongly correlated to CPL but if you categorized moves by type:

best move, good move, inaccuracy (CPL between 50-100), mistake (CPL between 100-300) and blunder (CPL >200)

then as it turns out, blunders are highly correlated but mistakes are not. Weird.

Anyhow, a start on looking at this intriguing study.

[dkappe]

Another attempt to predict rating from average cpl. No dice. https://web.chessdigits.com/articles/pr ... ipawn-loss

Both of the above have a relatively small sample size, so there’s that.

So, is the average centipawn loss being related to rating just an old wives tale? The search continues.

[CornfedForever]

Brazilian Youtuber, owner of the biggest chess channel in Brazil on YouTube and who is also a programmer, seems to have managed to prove that Hans Nielman has cheated since 2018.

Video in english:


His YouTube channel (Xadrez Brasil, only portuguese): https://youtube.com/c/XadrezBrasil

It does look very suspicious that he seemed to maintain a fairly steady accuracy rate from 2018 on, but I find it strange that his rating took so long to climb from 2300 to 2700 if he was playing at the same level all of that time. Hard to reconcile those two facts.

One assumption here is that centipawn loss (CPL) is strongly correlated with rating. I tried to find any publications on this, and the only thing I found was here: https://kwojcicki.github.io/blog/CHESS-BLUNDERS

Their conclusion was that rating wasn’t that strongly correlated to CPL but if you categorized moves by type:

best move, good move, inaccuracy (CPL between 50-100), mistake (CPL between 100-300) and blunder (CPL >200)

then as it turns out, blunders are highly correlated but mistakes are not. Weird.
Anyhow, a start on looking at this intriguing study.

Thinking back on the Wesley So vs Jeffrey Xiong GLOBAL CHAMPIONSHIP 'Armageddon' tie-breaker today, I wonder about the use of online play to determine...most anything. This being a 'win' for White or else...Xiong looked set to win...and fell into a draw, but that aside.

Most online play (between good players at least) tend to be 3/0 or even 1/0. Dealing with just 3/0, SO MANY of these degenerate into 'flag fests'. The clock is the ultimate weapon in no increment/delay play.

More rational would be to use only data with....say a 2 sec delay.

In any case,one should probably just stick with OTB data instead of that which comes about all too often from people sitting around in their pajamas, eating tacos with nothing is really on the line. From my first programming class, "Garbage In, Garbage out".

[dkappe]

Thinking back on the Wesley So vs Jeffrey Xiong GLOBAL CHAMPIONSHIP 'Armageddon' tie-breaker today, I wonder about the use of online play to determine...most anything. This being a 'win' for White or else...Xiong looked set to win...and fell into a draw, but that aside.

Most online play (between good players at least) tend to be 3/0 or even 1/0. Dealing with just 3/0, SO MANY of these degenerate into 'flag fests'. The clock is the ultimate weapon in no increment/delay play.

More rational would be to use only data with....say a 2 sec delay.

In any case,one should probably just stick with OTB data instead of that which comes about all too often from people sitting around in their pajamas, eating tacos with nothing is really on the line. From my first programming class, "Garbage In, Garbage out".

There was some amount of filtering in the data, i.e. no bullet games. There’s some discussion about various time controls there, as well.

I just figure if this aCPL to rating correlation is “known,” theres got to be a study confirming it somewhere.

[lkaufman]

Thinking back on the Wesley So vs Jeffrey Xiong GLOBAL CHAMPIONSHIP 'Armageddon' tie-breaker today, I wonder about the use of online play to determine...most anything. This being a 'win' for White or else...Xiong looked set to win...and fell into a draw, but that aside.

Most online play (between good players at least) tend to be 3/0 or even 1/0. Dealing with just 3/0, SO MANY of these degenerate into 'flag fests'. The clock is the ultimate weapon in no increment/delay play.

More rational would be to use only data with....say a 2 sec delay.

In any case,one should probably just stick with OTB data instead of that which comes about all too often from people sitting around in their pajamas, eating tacos with nothing is really on the line. From my first programming class, "Garbage In, Garbage out".

There was some amount of filtering in the data, i.e. no bullet games. There’s some discussion about various time controls there, as well.

I just figure if this aCPL to rating correlation is “known,” theres got to be a study confirming it somewhere.

I may have misinterpreted the article, but it seemed to be saying that the centipawn loss in a given game is not a good predictor of Elo rating, although in general higher elo players do have on average lower centipawn loss. If that's correct, then probably the average centipawn loss over a large number of games for a given player would probably be a good predictor of rating, especially if only comparable games are compared (i.e. ones with similar time limits). If this is not true, this would really contradict everything I believe about human chess strength and engine evals.

[dkappe]

I may have misinterpreted the article, but it seemed to be saying that the centipawn loss in a given game is not a good predictor of Elo rating, although in general higher elo players do have on average lower centipawn loss. If that's correct, then probably the average centipawn loss over a large number of games for a given player would probably be a good predictor of rating, especially if only comparable games are compared (i.e. ones with similar time limits). If this is not true, this would really contradict everything I believe about human chess strength and engine evals.

In both articles it was aCPL per game, but then an attempted regression of aCPL vs rating and that didn’t work. So no easy way to get a function from aCPL or game to rating.

But since the original video assumed that such a function or relationship exists, I’m looking for a paper or article that demonstrates it.

[Uri Blass]

Thinking back on the Wesley So vs Jeffrey Xiong GLOBAL CHAMPIONSHIP 'Armageddon' tie-breaker today, I wonder about the use of online play to determine...most anything. This being a 'win' for White or else...Xiong looked set to win...and fell into a draw, but that aside.

Most online play (between good players at least) tend to be 3/0 or even 1/0. Dealing with just 3/0, SO MANY of these degenerate into 'flag fests'. The clock is the ultimate weapon in no increment/delay play.

More rational would be to use only data with....say a 2 sec delay.

In any case,one should probably just stick with OTB data instead of that which comes about all too often from people sitting around in their pajamas, eating tacos with nothing is really on the line. From my first programming class, "Garbage In, Garbage out".

There was some amount of filtering in the data, i.e. no bullet games. There’s some discussion about various time controls there, as well.

I just figure if this aCPL to rating correlation is “known,” theres got to be a study confirming it somewhere.

I may have misinterpreted the article, but it seemed to be saying that the centipawn loss in a given game is not a good predictor of Elo rating, although in general higher elo players do have on average lower centipawn loss. If that's correct, then probably the average centipawn loss over a large number of games for a given player would probably be a good predictor of rating, especially if only comparable games are compared (i.e. ones with similar time limits). If this is not true, this would really contradict everything I believe about human chess strength and engine evals.

I think that there are other factors that effect average centipawn loss that are not playing strength of the player so average centipawn loss cannot be a good predictor.

If a player insist to play drawn endgames for a long time then the player can reduce the average pawn loss because it is easy to get 0 centi-pawn loss in obvious drawn position by keeping a draw score.

Maybe it is better to divide average centipawn loss by average centipawn loss of some weak chess engine in the same positions.

[Uri Blass]

Brazilian Youtuber, owner of the biggest chess channel in Brazil on YouTube and who is also a programmer, seems to have managed to prove that Hans Nielman has cheated since 2018.

Video in english:


His YouTube channel (Xadrez Brasil, only portuguese): https://youtube.com/c/XadrezBrasil

It does look very suspicious that he seemed to maintain a fairly steady accuracy rate from 2018 on, but I find it strange that his rating took so long to climb from 2300 to 2700 if he was playing at the same level all of that time. Hard to reconcile those two facts.

I do not find the evidence convincing

I suspect that the same player can get better accuracy against weaker players so it is possible that hans got worse accuracy because of playing against better players and at the same time better accuracy because he became better and the sum of both factors is close to 0.

If that is the case, why wouldn't it apply pretty much equally to Gukesh? Why the huge disparity between Gukesh's declining error rate and Niemann's steady error rate while both were making similar progress in their teen years? But then I don't have a better explanation for the similar rating climb with highly dissimilar error rate histories. It is a puzzle.

You can make progress in rating by different ways:
one is getting 50% against players with higher rating
one is getting 60% against players with equal rating
one is getting 70% against players with slightly lower rating.

I am not sure if Gukesh and Hans progressed in a similiar way.
Edit:Looking at the data it seems that both got significantly more than 50% so it is probably not the explanation but there may be different explanations except cheating and player may change his playing style when he get progress and go for positions when it is easier to make mistakes(both for himself and for the opponent) when he gets stronger.

[keinrath]

Niemann, Hans Moke
2021.04.01 2508
2022.10.01 2699
+191 ELO

Gukesh D
2021.04.01 2578
2022.10.01 2732
+154 ELO