Winning starts with what you know
The new version 18 offers completely new possibilities for chess training and analysis: playing style analysis, search for strategic themes, access to 6 billion Lichess games, player preparation by matching Lichess games, download Chess.com games with built-in API, built-in cloud engine and much more.
According to himself "Rafaelvleite" has
"a bachelor in Production Engineering at the University of Sao Paulo (POLI-USP), and [is] also a Programmer and Data Scientist. Besides that, [he has] a YouTube channel about chess in Brazil, called “Xadrez Brasil”, with almost 300k subscribers and more than 80 million views.
"Rafaelvleite" published his finding on his YouTube channel and in an article, in which he claims to have found the "perfect correlation between ... rating and ACPL (Average Centipawn Loss) and STDCPL (Standard Deviation Centipawn Loss)".
ACPL and STDCPL provide data that allow to estimate the playing strength of a whole range of players by comparing their games with engine programs:
"Basically, Centipawn Loss is a measure of how distant the evaluation of the move a player made in a game is from the suggested computer best move. It is reasonable to think that as a chess player gets better and more professional, the lower will be the Average of the Centipawn Loss.
And Standard Deviation Centipawn Loss is a measure of how consistent the moves are. Amateur players tend to do some good moves, then bad moves, but a professional player is more consistent on the quality of his moves. It is reasonable to think that there is some correlation between Rating and STDCPL then."
The Brazilian had the idea to take a closer look at the games of Hans Niemann and other strong players after "after seeing a video from FIDE Master Yosha, a player that was trying to find some evidence by comparing Niemann’s moves with the ones suggested by the computers".
To pursue this idea, "Rafaelvleite" built a database of 7568 games from players that "included ... top GMs like Andrey Esipenko, Gukesh, Firouzja, Keymer, Praggnanandha, Erigaisi, Carlsen, Niemann, Caruana, Bobby Fischer and others". He then analysed all games with Stockfish 15 and writes about his results:
"With these studies, I made 2 huge findings: there is an established correlation between a player Rating and the ACPL (Average Centipawn Loss) and STDCPL (Standard Deviation Centipawn Loss).
And when I took the Centipawn Loss averages from all players in the database and grouped by Rating Tiers, and the same to the standard deviations, I found this:
According to the Brazilian statistician there is a marked "correlation between Rating, ACPL and STDCPL". To illustrate his claim, he lists the ACPL and STDCPL values of a number of players:
However, the ACPL and STDCPL values of Hans Niemann differ from the ACPL and STDCPL values of these players:
"Rafaelvleite" then details the numbers of Hans Niemann:
- ACPL stopped to go down at 25 value even when Hans acchieved 2700 rating. According to our expected curves, 25 should stand for a 2500–2550 rated player. A 2700 rated player should have gone down to 22, as all 2700 players above did.
- STDCPL is at 48. The lowest value he achieved was 44, again, those are values found in a 2500–2550 player. A 2700 rated player is expected to go down to value 38. This level of 48 shows a high variation regarding the quality of his moves for a 2700 rated player.
Moreover, according to "Rafaelvleite", the data of Niemann changed dramatically after 2018:
"The years before 2018 shows an expected curve: linear, with decay on ACPL and STDCPL, expected values. But the years after 2018 shows unexpected behavior. Low correlations and ACPL and STDCPL not compatible with 2700 rating.
After listing all these data, the Brazilian streamer asks the "Big Question":
"The big question is: What can possibly explain a 2500 strength player acchieve 2700 rating?" and concludes:
"I have pointed a new direction showing the perfect correlation between ACPL, STDCPL and Rating. I have explained the steps that I took, anyone can easily replicate it.
My contribution is done. If FIDE wants to use it as a complementary method to use in the ongoing investigations, it may be helpful. It may not be. I don’t know. I just feel happy with what I found and thankful to have the opportunity to spread it to the world."
