I think those are two different definitions. In the video, the engine correlation represents the amount of moves that matched the top move of a chess engine, as defined here: https://en.chessbase.com/post/let-s-check-engine-correlation... The accuracy metric in the article is defined a bit differently according to how Lichess computes it: https://lichess.org/page/accuracy

Yes. To be honest, when the match was over, I was also left with the feeling that Ding did not capitalize enough on his opportunities. But later after crunching the data I saw that it was actually the other way around.

Sincerely, doesn't this make you question your methodology? It's such an obviously incorrect conclusion that you may as well have concluded that Ding actually won.

I feel like your over-reliance on engine stats like ACPL has led you to some conclusions that may have been true had stockfish been playing leela but really have little or nothing to do with humans playing chess.

Thanks for sharing your opinion. I actually addressed many of the points you raised in the conclusions section of my article. I acknowledged the limitations of analyzing a chess match purely through numerical metrics. However, I still believe that looking at the match through this analytical lens offers a valuable perspective, complementing other types of analysis, such as commentary from players and bloggers. It provides a unique angle that, while imperfect, can uncover insights that might otherwise be overlooked. Ultimately, I see this as an additional tool in understanding the match, rather than a replacement for more traditional forms of analysis.

What is the added insight though?

I believe it offers a neutral perspective on the game, without any bias that could exist in any analysis that is not data-driven. Sometimes when a chess commentator dislikes a particular player's style, it could be reflected in his commentary. For me personally, analyzing the match this way changed my view on it, but I completely understand if you do not feel that way.

Thank you, you're right, I corrected this mistake. As the difference in acpl is negligible anyway, it does not affect the overall conclusions and insights.

I analyzed the 2024 World Chess Championship match using empirical and synthesized approach. I focused on metrics like conversion rates, resilience rates, and the impact of errors on the match outcome. The analysis concentrates on providing a more overall outlook on the match, without doing a game-by-game breakdown. Let me know your thoughts!

Interesting point.