What ChessIQ measures, why labels can change with deeper analysis, and how a reviewed move becomes training.
What is ChessIQ actually measuring?
It compares each move with stronger engine alternatives, then summarizes the swings that mattered.
Why can results change during deeper analysis?
Because deeper search can reveal tactics or defenses that were not visible earlier. ChessIQ refines results over time instead of pretending the first pass is final.
What does ChessIQ turn into training?
Missed opportunities and major mistakes from your analyzed games can become training positions when they pass the save checks.
ChessIQ is not trying to guess talent or predict your rating. It measures move quality in context: what the position allowed before your move, what changed after your move, and how large that swing was.
Move labels (such as Best, Good, Inaccuracy, Mistake, Blunder, or Miss) are readings of engine evaluation data. Accuracy summarizes decision quality; it is not a full description of everything that happened in the game.
In ChessIQ, the pre-move evaluation represents the position you had to solve. That is where best-move alternatives come from.
The post-move evaluation represents the position you actually created. That is what powers swing charts, outcome context, and many critical-moment signals.
Initial review is a fast pass. After that, continuous analysis can keep deepening the focused position. As depth improves, best lines, evaluations, and sometimes labels can be updated.
ChessIQ intentionally gates harsher re-labeling behind stronger stability support so transient shallow reads are less likely to flash misleadingly.
Best move means the strongest continuation found for the pre-move position at the current analysis strength.
Accuracy summarizes move quality over the game and de-emphasizes low-information situations like forced sequences and routine moves in already decided positions.
Critical moments are high-impact swings, missed conversions, initiative flips, and similar turning points surfaced from move-by-move analysis signals.
After review, ChessIQ can generate training positions from your own game history when candidate positions pass the save checks. The current lanes separate missed opportunities from blunders/major mistakes so practice stays targeted.
Puzzle priority uses local attempt history, repeats, due timing, and recurrence, so weak spots can come back until they stabilize.
Core analysis runs in your browser using Stockfish WebAssembly. Core review history, training state, and many preferences are stored locally (including IndexedDB/local storage paths used by the app).
External calls are still used for public-game imports (Chess.com/Lichess) and production analytics, so local-first should be read as a trust boundary — not an absolute "no network ever" claim.
Privacy covers storage and service boundaries. FAQ covers common interpretation questions.