Classifying moves in chess involves both art and science. How do we distinguish between a good move and an inaccurate one? How do we define a blunder for a chess master versus a new player? Is it more significant to go from +2 to +1 or from +0.7 to +0? What engine evaluation makes a position winning?
With Classification V2, Chess.com has adopted an Expected points model to answer these questions.
Expected points model
Expected Points employs data science to calculate a player's probability of winning by considering their rating and the engine's evaluation of the position.
Expected Points uses data science to determine a player’s winning chances based on their rating and the engine evaluation, where 1.00 is always winning, 0.00 is always losing, and 0.50 is even.
At 1.00, you have a 100% chance of winning, and at 0.00, you have a 0% chance of winning. After you make a move, we evaluate how your expected points—likely game outcome—have changed and classify the move accordingly.
The table below shows the expected points cutoffs for various move classifications. If the expected points lost by a move is between a set of upper and lower limits, then the corresponding classification is used:
Classification | Lower Limit | Upper Limit |
| 0.00 | 0.00 |
| 0.00 | 0.02 |
| 0.02 | 0.05 |
| 0.05 | 0.10 |
| 0.10 | 0.20 |
| 0.20 | 1.00 |
Best
Excellent
Good
Inaccuracy
Mistake
Blunder
Special move classifications beyond expected points
Special move classifications that use rules beyond expected points have also undergone improvements:
Classification | When is it used? |
| These are moves that were critical to the outcome of the game, such as turning a losing position into an equal one, an equal position into a winning one, or finding the only good move in a position.
Similar to Brilliant Moves, we are more generous in what we call a Great Move for new players compared to higher-rated players. |
| Brilliant Moves are always the best or nearly best move in the position, but they are also special in some way.
We replaced the old Brilliant algorithm with a simpler definition: a Brilliant move is when you find a good piece sacrifice.
There are additional conditions:
We are also more generous in defining a piece sacrifice for newer players compared to those who are higher-rated. |
| A Miss is when you fail to capitalize on your opponent's mistake and miss the opportunity to gain a winning position, often resulting in an equal or worse outcome.
The engine evaluation required to determine a winning, equal, or losing position varies according to the player's rating, similar to expected points. |
Great Move
Brilliant
Miss
Check out this article to learn more about Game Review: How does Game Review work?