Chess AI Lab — v2 updated project map

Individual chess players exhibit stable, measurable behavioural signatures across their games — patterns of move timing, position-type preference, and decision-making style that persist over time and are distinct enough to identify a player. This project constructs a longitudinal behavioural fingerprint from 10,000 personal chess games, tracks its evolution month-by-month, and compares it against the profiles of players in the same rating band who achieved significant rating improvement. The central hypothesis is that meaningful skill improvement is preceded by measurable behavioural shift, and that those shifts can be identified and targeted in advance.

Chess improvement tools overwhelmingly focus on what the engine would have played, not on what a human at a slightly higher skill level actually plays in the same situation. This project builds a personalised mistake classifier trained on one player's 10,000-game error history, tagged by position type, game phase, opponent rating, and time pressure. When a recurring weakness is detected, the system retrieves real games from Lichess played by humans 200–400 Elo higher in the identical structural context — then uses an LLM to extract the principled differences in their approach, not just the engine's top move. The output is human-readable coaching grounded in realistic human behaviour, not superhuman engine perfection.

Opening preparation tools universally teach GM mainlines, which are often irrelevant or counterproductive below 1800 Elo — positions that theory considers equal are won and lost asymmetrically at club level depending on the specific imbalances they create. This project builds a personalised opening repertoire grounded in two filters: what actually succeeds statistically at the player's specific rating band, and which lines align with the player's behavioural fingerprint from C1. Lines that lead to position types where the player consistently underperforms are deprioritised regardless of their theoretical evaluation, while lines that reach structurally favourable ground for that player's style are surfaced and annotated.

Elite chess players routinely employ human seconds to prepare against specific opponents — analysing their opening tendencies, collapse patterns under time pressure, and psychological vulnerabilities. This preparation is unavailable to club players. This project automates opponent profiling by ingesting an opponent's public game history from chess.com, extracting behavioural features using the same pipeline as C1, and cross-referencing their weaknesses against the player's own strength profile. The output is a pre-game brief that surfaces concrete matchup edges: openings where the opponent struggles, time controls that expose their weaknesses, and structural imbalances that favour the player's own style.

Chess move timestamps encode a rich psychological signal that is almost entirely unexploited by existing analysis tools. The time a player spends on a move — relative to their own baseline, the game phase, and the position's complexity — reveals their cognitive state: confident theory recall, deep calculation, intuitive judgment, or time-pressured panic. This project extracts per-move think-time from PGN clock data, correlates it with eval drops and game outcomes, and constructs a personal "danger zone" map: the specific combinations of time remaining, move number, and position type where this player's decision quality degrades most sharply. As a secondary output, the self-labeled dataset produced becomes a research contribution to the fair-play detection community.

Maia-2 (NeurIPS 2024) established that human chess behaviour can be modelled coherently across skill levels using a unified neural architecture, but explicitly acknowledged that the model does not produce actionable improvement pathways — it cannot tell a 1400-rated player which specific behavioural changes would move them to 1600. This project addresses that gap directly. Using the full Lichess database segmented into 100-Elo bands, we extract a multi-dimensional behavioural profile for each band: time allocation per game phase, move-type distribution, tactical vs positional preference, blunder category frequencies, and endgame conversion rates. The result is a continuous behavioural atlas that maps what concretely and measurably changes in how players think and decide as skill increases — turning the Maia-2 model into an actionable coaching instrument.

Chess coaching has long recognised that players improve fastest by studying GMs whose style resembles their own — a positional player learning from Petrosian internalises ideas more readily than from Tal. However, this matching has always been intuitive and manual, performed by experienced coaches. This project automates the match by computing cosine similarity between a student's C1 behavioural feature vector and the extracted profiles of a curated corpus of historical GMs, weighted by the student's specific weakness categories. The result is a ranked list of style-matched GMs, filtered to their games in positions the student actually reaches, annotated by an LLM with principle-based explanations rather than engine lines. This is the automated version of what a human second does — and the basis for a B2B coaching tool.

Fair-play detection research is constrained by a fundamental data problem: existing labeled datasets either conflate all human moves as one category or use synthetically generated "cheating" patterns that don't reflect real subtle engine use. What's missing is a granular, move-level labeled dataset that distinguishes the spectrum of human move types — from confident theory recall to panicked time-pressure decisions — because these categories have radically different temporal and quality signatures that overlap with both clean play and subtle cheating. This project produces exactly that dataset as a byproduct of C8: 10,000 personal games with move-level labels across five behavioural categories, combined with Maia move-match probabilities and Stockfish evaluations, released publicly in a format compatible with the Irwin and CSSLab detection frameworks.