The Accuracy metric indicates how well you play - according to Stockfish, the strongest chess engine.

An accuracy of 0% means you only played terrible moves; 100% means you played all the preferred Stockfish moves.

How is it computed?

Accuracy% represents how much you deviated from the best moves, i.e. how much your winning chances decreased with each move you made. 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. Because if you have a good move to play, then it means the position was already good for you before you played it.

First, compute Win%

Win% represents your chances of winning the game from a given position. It's based on a Stockfish evaluation in centipawns. We then apply an equation to make it more intelligible:

Win% = 50 + 50 * (2 / (1 + exp(-0.00368208 * centipawns)) - 1)

It looks like this:

Win% by Stockfish centipawn evaluation

Here's a link to the graphed equation, and another the Lichess source code implementing it.

The equation is based on real game data. Note that we might update it in the future, to better map centipawns to win chances.

Then, compute Accuracy%

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. Here's the equation:

Accuracy% = 103.1668 * Math.exp(-0.04354 * (winPercentBefore - winPercentAfter)) - 3.1669

It looks like this:

Accuracy% by difference of Win% from a position to the next one

Here's a link to the graphed equation, and another the Lichess source code implementing it.

Note that we might change this equation in the future, to better map Win% to move accuracy (Accuracy%).

Why not just use Stockfish centipawns?

Centipawns are great for developing chess engines, which is their main use. But not so much for human comprehension.

A major issue with centipawns is that they're dependent of the position evaluation. For example, losing 300 centipawns in an equal position is a major blunder. But losing 300 centipawns when the game is already won or lost makes almost no difference and is largely irrelevant.

Thus, "300 centipawns" has no meaning on its own for a human. That's the problem we aim to solve with Win% and Accuracy%. These new values are derived from centipawns, but they try to be independent of the position evaluation. 30 Accuracy% should mean the same thing whether the position is equal or winning/losing.

Also see Centipawns Suck by Nate Solon.

Where are Win% and Accuracy% used?

Lichess has been using these for years, to identify inaccuracies/mistakes/blunders in game analysis.

They're also visible and usable in Lichess Insights. Examples:

• Accuracy by opening family during the opening and middlegame (averaged with harmonic mean)
• Tactical Awareness by game phase (how often you spot opponent mistakes)

They may become visible in other parts of the site in the future.