lila VS Stockfish

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Lichess - Online Chess Server. Scala, TypeScript

> the player who committed more blunders lost 86% of the time

In some sense this is almost tautological. While finding an exact definition for a chess blunder isn't straightforward, here is one example from the Lichess UI:

https://github.com/lichess-org/lila/blob/b527746b179cdde6438...

Basically, if you make a move which decreases your winning probability more than 14% over the best move, that's a blunder. But winning probability is a nonlinear function of stockfish centipawns. A drop in 100 centipawns when you're up 15 points isn't a blunder. When the game was equal, it is.

Point is, by the time you know it's a blunder you already know something about the outcome of that move, that it swung the winning probability by more than 14%. So the analysis is kind of just measuring some function of winning probability and saying that it is highly correlated with winning probability.

If you want to improve make it your goal to play the best chess you can, not increase an arbitrary number. Watch YouTube series like John Bartholomew's "Climb the Rating Ladder" for some general insight into what you might be doing wrong. Read Irving Chernev's "Logical Chess: Move By Move" to see the thinking process of high level players. Do lots of puzzles (I like lichess.org for puzzles). And always analyze your games. When you analyze make it your goal to find at least two things you could have improved.

The easiest way to play against Stockfish is perhaps on https://lichess.org/, but it's not the only chess engine that evaluates positions with a neural network.

Lichess.com

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The Stockfish program can be set to play at strength level 0-20. Estimates of the levels' Elo is provided here: https://github.com/official-stockfish/Stockfish/commit/a08b8...

- and handicapped Stockfish (https://stockfishchess.org).

The whole thing is at https://github.com/magv/bchess, and can be installed with just 'pip install bchess'.

This was the result searching for all the 35 stockfish benchmark positions to depth 6.

Stockfish doesn't have a classical evaluation anymore. And before this, most of the time (around 90%), NNUE was used to evaluate.

If you use ChessBase on a MacBook through Parallels, there's an issue where people have posted Apple Silicon compiles for Windows: https://github.com/official-stockfish/Stockfish/issues/4241

Progress can be found here https://github.com/official-stockfish/Stockfish/wiki/Regression-Tests At 1 thread it has gained +18.3 elo on a balanced book, and +47.03 on UHO (unbalanced) book as well as +39.4 elo for FRC and +65.56 for DFRC. At 8 threads it has gained +14.33 elo on a balanced book and +49.46 on UHO (unbalanced book). Also testing was done on 8 threads with 180+1.8 (this is considered very long time control for fishtest standards) and progress was +9.45 on balanced book and +49.65 on UHO.

Downloads are available temporarily here https://github.com/official-stockfish/Stockfish/releases/tag/stockfish-dev-20230622-a49b3ba7

The strongest version of Stockfish is the latest development version of Stockfish

I am trying to implement Stockfish (a popular chess engine) into my React Native app.