03 · Research-stage
AI Player Modeling
We investigate whether a player's game history can support useful, testable hypotheses about their choices.
Discuss this workMove-choice hypothesisHeld-out check pending · research stageIllustrative · no client data
The question we answer
Does this model add evidence that a coach or analyst could not get more reliably another way?
Player modeling is a research capability. It can surface hypotheses about move choice and style, but no model should be treated as a reliable substitute for a human opponent without task-specific validation.
01The evidenceWhat enters
What enters
the analysis.
We use evidence appropriate to the actual match. A pattern from a small or old sample does not become a certainty by appearing in a chart.
- 01Quantity, quality and dates of available games
- 02Clear prediction task and baseline
- 03Held-out games and expert review
- 04Failure cases and uncertainty
02The decisionsWhat we need
What we need
to resolve.
01Analysis02Engine checked03Human approved04Player ready
Each state means something different. A deep engine search does not automatically make a line ready for a player.
03The handoffWhat leaves
What leaves
the analysis room.
A transparent research assessment, with clear limits on what the model can and cannot support.
01Feasibility assessment
02Validated findings where supported
03Explicit limitations and recommended next tests
Final scope depends on available games, the event timetable and the questions agreed with the player or coach. Exclusivity and confidentiality standards
Your move
Discuss your preparationBuild the right brief.
Tell us the event, opponent and deadline. We will check availability and define the work together.