Centipawn loss (or simply the engine's evaluation of a position) doesn't take into account how realistically a human could hold a position.

During yesterday's WCC Game 6 the computer evaluation meant little when players were in time trouble. Anything could have happened going into the first time control, despite the game being dead drawn for the first 3.5 hours.

In the final stages the computer again evaluated the game as drawn, but presumed Nepo could defend perfectly for tens of moves without a single inaccuracy. Super GMs can't do that given hours or days, let alone minutes.

Last thought: did anyone else assume this was written in R/ggplot2 at first glance? Seaborn and/or matplotlib look strikingly like ggplot2 now days!

Precision is a murky concept in chess because it is not a solved game. First, if the move doesn’t change the best play result, can it really be called imprecise? Only in terms of practical chances.

And if we are talking about practical chances, why should we rely on computer-centric evaluation? If a human has to choose between a move that leads to the win but they have to find 40 best moves or they will lose and a move that is a theoretical draw but now the opponent has to find 40 moves or they will lose, what should a human choose?

What is even the ACPL of a move from a tablebase? There is no value, it is either a win, a draw or a loss. So while the whole idea behind this exercise is intuitively appealing and certainly captures some sense behind the idea of accuracy, it should be taken with a grain of salt.

> If we’d used a different chess engine, even a weaker version of the same one — such as Stockfish 12 — it may have found the 2018 World Championship the most accurate in history (assuming both players prepared and trained using Stockfish 12 in 2018).

This would be a really good follow-up experiment. If the theorized result really happens, we would have strong evidence that players are "overfitting" to their training chess engine. It would also be interesting to see how stable the historical figures look between different engines.

Yes "Alan Turing, was the first recorded to have tried, creating a programme called Turochamp in 1948."

But also

"Since 1941 Zuse worked on chess playing algorithms and formulated program routines in Plankalkül in 1945."

https://www.chessprogramming.org/Konrad_Zuse

I'm most impressed by Capablanca's jump in highly accurate play back in 1921, that would not be surpassed for another 60 years.

One of the reasons that Poker players prefer tournaments is because it induces them to move away from perfect Nash equilibrium play and into being exploitable, as someone who plays unexploitable play simply doesn't make it to the money as someone who does. Winning 51% of the time means nothing when you need to be in the top 10% to earn anything back.

It seems like just looking at ACPL isn't looking at this correctly. If someone makes a mistake, and loses some centi-pawn, but it induces an even larger mistake in their competitor, that wasn't a mistake, it was a risk.

Why not use something similar to alphago zero to carefully analyze chess games of a deceased player until it is able to mimic its decisions?

It could bring many players "back to life". It would be even possible to watch "impossible matches" like Kasparov vs Capablanca!

If “accuracy” measures how well a player matches computer chess, then as players continue to study more and more with chess programs, you would expect their play to match the programs more and more.

Personally I find it odd to measure how well the players match the computer program and call it accuracy. The computers do not open the game tree exhaustively so they give only one prediction of true min-max accuracy.

When Lee Sedol made move 78 in game 4 against AlphaGo, it reduced his accuracy but won him the game.

I think it would be worth looking at a player’s accuracy in terms of their cohort’s standard deviation, given that theory is more or less shared across all players. Even then, the best players now have the best teams and computers, so a lot of Magnus’s accuracy in this game is a credit to Jan Gustafsson et al. I’ve been thinking how you might capture the player’s accuracy out of their prep, that seems a better measure, but even then you’re so often choosing between five +0.0 moves by the middle-game, and you could easily play many totally accurate moves if you didn’t feel like agreeing a draw. I know some have looked at Markov models of a player’s likelihood of a blunder to analyse this instead.

Personally I’ve never felt Magnus enjoyed the modern game with as much opening preparation as we have now. It seems like he’s only in the last few years invested the time in this, instead of relying on his technique to win even from losing positions. I hope AlphaZero proving that fun positional ideas like pawn sacrifices and h4 everywhere reinvigorated him somewhat during his dominant first half of 2019, so there’s still hope the machines haven’t just drained the romance from the game, even if their ideas remain dominant.

I would have liked to see this go back far enough to include Morphy, whom Fischer considered "the most accurate player who ever lived." I would be surprised if Stockfish agreed, but it would be interesting to see.

> At the time of publishing, the last decisive game in the World Championship was game 10 of the World Championships 2016 — 1835 days ago, or 5 years and 9 days. Is the singularity being reached, with man and machine minds melding towards inevitable monochromatic matches?

Very very unfortunate timing but still a valid question.

Is there a risk that this measure is telling us as much about how likely a match was to contain difficult positions as about how skilled the players were?

For example, Karpov and Kasparov sometimes agreed short draws. I wonder if that is flattering their figures.

It's strange how many times the article says 'chess software' has improved since (Turing's day, the 1990s, whenever). Sure, the software is better, but six orders of magnitude in hardware performance haven't hurt either.

In chess ACPL roughly works like goals scored (conceded) in football. Goals are made when the defending team makes mistakes. A team that is a master of defense will concede few goals. But will also score few goals since defending well requires playing cautiously. Its the same with attacking, aggressive teams. They both score and concede more goals than the average.

For historical human-to-human games, it would be more interesting to see how well players targeted with weaknesses of their opponents. That skill likely mattered more than absolute accuracy as measured by computers.

I'm not sure how meaningful these numbers are. I get around 40-50 ACPL in my games, and I certainly wouldn't have been anywhere near a match for Botvinnik.

Sorry i am highjacking this thread. I am on a quest to find the rules of the chess variant finesse by GM walter Browne. If anyone knows them :

https://lookingforfinesse.github.io/lookingforfinessevariant...

Just in case you, like me, were wondering what the word "accurate" means in this context:

https://support.chess.com/article/1135-what-is-accuracy-in-a...