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Is it possible to break up the definition and difference of those terms further into specific factors,

Other than the algorithm?

joseph1000000 wrote:

Is it possible to break up the definition and difference of those terms further into specific factors,

Other than the algorithm?

I don't think so. You'd have to know how they define:

"**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."

After each game, you'll see a list of all your moves in the game review, classified as 'Best' or 'Inaccuracy', or many other such classifications. How are these determined?

Classifying moves is a mix of art and science. Where is the line between a good move and an inaccurate one? How is a blunder defined for a chess master compared with a new player? What matters more, going from +2 to +1 or from +0.7 to +0? What engine evaluation is needed for a position to be considered “winning”?

**With ClassificationV2, Chess.com has moved to an “expected points” model**, rather than strict evaluation differences, to answer these questions.

**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.

Basically, 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 look at 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.

Table I: Move Classifications with their corresponding change in expected points boundaries. 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 |

Best | 0.00 | 0.00 |

Excellent | 0.00 | 0.02 |

Good | 0.02 | 0.05 |

Inaccuracy | 0.05 | 0.10 |

Mistake | 0.10 | 0.20 |

Blunder | 0.20 | 1.00 |

Special move classifications that use rules beyond expected points have also undergone improvements. This includes familiar classifications like Missed Win and Brilliant, as well as the new Great Move classification.

**A Missed Win is when you miss an opportunity to capitalize on your opponent’s mistake and gain a winning position, and instead end up equal or worse.** As with expected points, the engine evaluation needed to be in a winning, equal, or losing position will change along with a player’s rating.

Brilliant (!!) moves and Great Moves are always the best or nearly best move in the position, but 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 some other conditions, like you should not be in a bad position after a Brilliant move and you should not be completely winning even if you had not found the move. Also, we are more generous in defining a piece sacrifice for newer players, compared with those who are higher rated.

**Great Move is a new move classification that is denoted by a single exclam (!). These are moves that were critical to the outcome of the game, such as going from losing to equal, equal to winning, or finding the only good move in a position.** Similar to Brilliant moves, we are more generous on what we call a Great Move for new players compared with high-rated players.

Overall, the improved Move Classification system provides a tailored Game Review experience. The new definition of Brilliant and the Great Move category give you a way to identify and share the most interesting moments from your games. The expected points formula identifies the mistakes that matter most for your improvement.

justbefair wrote:

Also, we are more generous in defining a piece sacrifice for newer players, compared with those who are higher rated.

Similar to Brilliant moves, we are more generous on what we call a Great Move for new players compared with high-rated players.

Ahah! I've been suspecting this. When I asked about this in another thread, people responded that the algorithm doesn't make that distinction.

Knights_of_Doom wrote:

justbefair wrote:

Also, we are more generous in defining a piece sacrifice for newer players, compared with those who are higher rated.

Similar to Brilliant moves, we are more generous on what we call a Great Move for new players compared with high-rated players.

Ahah! I've been suspecting this. When I asked about this in another thread, people responded that the algorithm doesn't make that distinction.

Which thread is that?

Your best bet here is to discard the words. Look at the numbers. I mean you can say that losing 1.3 points of score is a blunder, losing 1.2 or less is inaccuracy, whatever heuristics you want to dump into your translator to spew out human words at the user, but the numbers are what they are, the words are subjective to some algorithm that is only partially described to us, at best.

joseph1000000 wrote:

Knights_of_Doom wrote:

justbefair wrote:

Also, we are more generous in defining a piece sacrifice for newer players, compared with those who are higher rated.

Similar to Brilliant moves, we are more generous on what we call a Great Move for new players compared with high-rated players.

Ahah! I've been suspecting this. When I asked about this in another thread, people responded that the algorithm doesn't make that distinction.

Which thread is that?

I couldn't possibly remember.

Knights_of_Doom : thank you for continuing to this thread!

Does the cat play chess too😁 it's stare is a thoughtful One 😁

jonnin: that would be a rough summary of what Justbefair said. Thanks for taking time to participate!

I would have liked to see more differentiation between those terms and other exact factors!

The change in expected points boundaries is a little hard to understand but it is a statistically measured change in the chances that one side or the other is going to win.

The descriptions of "Excellent" and "Good" are a problem since the computer thinks that the winning chances of the player who played it got worse.

The old way, looking at points of material, had problems too.

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When playing with computer, among feedbacks I get "inaccurate", mistake and/or blunder. Can anyone explain them and the difference between them? Thanks for your participation!!!