this post was submitted on 03 Aug 2023
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# | Player | Country | Elo |
---|---|---|---|
1 | Magnus Carlsen | ๐ณ๐ด | 2839 |
2 | Fabiano Caruana | ๐บ๐ธ | 2786 |
3 | Hikaru Nakamura | ๐บ๐ธ | 2780 |
4 | Ding Liren ๐ | ๐จ๐ณ | 2780 |
5 | Alireza Firouzja | ๐ซ๐ท | 2777 |
6 | Ian Nepomniachtchi | ๐ท๐บ | 2771 |
7 | Anish Giri | ๐ณ๐ฑ | 2760 |
8 | Gukesh D | ๐ฎ๐ณ | 2758 |
9 | Viswanathan Anand | ๐ฎ๐ณ | 2754 |
10 | Wesley So | ๐บ๐ธ | 2753 |
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Not sure I understand everything, although I think I understand that an influx of new players can totally skew statistics.
It's worth noting that this is about ELO ratings, so lichess may have a similar problem (I think it might be the case for Blitz on Lichess to suffer from a similar problem), but some of the proposed fixes are FIDE ELO specific (increasing starting ELO).
The problem seems to be that the ELO ratings aren't accurate to estimate the correct probabilities for a match between a long-time chess player with higher ELO rating and a player from the "Queen's gambit wave".
Now the authors seem to paint this as a problem with the new players being underrated, the ELO distribution to be skewed. I agree that this can be a skew, I wonder however if the solution should be to boost ELO ratings of lower-ranked players.