![]() They may become visible in other parts of the site in the future. Tactical Awareness by game phase (how often you spot opponent mistakes).Accuracy by opening family during the opening and middlegame (averaged with harmonic mean).They're also visible and usable in Lichess Insights. Lichess has been using these for years, to identify inaccuracies/mistakes/blunders in game analysis. 30 Accuracy% should mean the same thing whether the position is equal or winning/losing. These new values are derived from centipawns, but they try to be independent of the position evaluation. That's the problem we aim to solve with Win% and Accuracy%. Thus, "300 centipawns" has no meaning on its own for a human. But losing 300 centipawns when the game is already won or lost makes almost no difference and is largely irrelevant. For example, losing 300 centipawns in an equal position is a major blunder. ![]() But not so much for human comprehension.Ī major issue with centipawns is that they're dependent of the position evaluation. Note that we might change this equation in the future, to better map Win% to move accuracy ( Accuracy%).Ĭentipawns are great for developing chess engines, which is their main use. Here's the equation: Accuracy% = 103.1668 * exp(-0.04354 * (winPercentBefore - winPercentAfter)) - 3.1669Īccuracy% by difference of Win% from a position to the next one Now that we have a Win% number for each position, we can compute the accuracy of a move by comparing the Win% before and after the move. Note that we might update it in the future, to better map centipawns to win chances. Here's a link to the graphed equation, and another the Lichess source code implementing it. We then apply an equation to make it more intelligible: Win% = 50 + 50 * (2 / (1 + exp(-0.00368208 * centipawns)) - 1) It's based on a Stockfish evaluation in centipawns. ![]() Win% represents your chances of winning the game from a given position. Because if you have a good move to play, then it means the position was already good for you before you played it. Indeed in chess, from a chess engine standpoint, good moves don't exist! You can't increase your winning chances by playing a move, only reduce them if you make a mistake. how much your winning chances decreased with each move you made. How exactly is it computed?Īccuracy% represents how much you deviated from the best moves, i.e. As we discussed above, protracted lopsided endgames can increase the accuracy score. Moreover, lower-rated players are often more reluctant to resign. This can create more complicated positions and provoke inaccurate play on both sides. ![]() A more skilled player tends to play more principled, theory-heavy openings and put more tactical pressure on the opponent. ![]() While there is some correlation between the players' ratings and their accuracy, it is not straightforward. Do higher-rated players have a higher accuracy score? That said, if you suspect your opponent was cheating, please use the report form. If you blunder early on or play consistently subpar moves, your opponent will have greater chances to capitalize on your mistakes. Were they cheating?Ī very high accuracy percentage isn’t necessarily indicative of superhuman, "GM-level" play. While Stockfish can assess the soundness of our moves, it can’t tell us how difficult it is to find them. Conversely, in lopsided positions most moves don’t change the winning chances meaningfully, so the accuracy score may be high even if your conversion of the position wasn’t clinical. In more complex positions it is harder to find the best moves, so your accuracy might drop accordingly. It is flawed to compare accuracy to a numerical grade you would get on a test. What accuracy percentage is considered "good"? The Accuracy metric indicates how well you play - according to Stockfish, the strongest chess engine.Īn accuracy of 0% means you only played terrible moves 100% means you played all the preferred Stockfish moves. ![]()
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