Methodology

How JustShove recommendations work

JustShove is a fast educational tournament poker tool. It does not run a real GTO solver. The MVP uses deterministic heuristic logic to turn common tournament inputs into a clear shove, fold, call, or raise recommendation.

Not a solver

No simulation claims

The current engine does not compute equilibrium strategy, exact range equity, or Nash charts.

Deterministic

Same spot, same output

The same inputs produce the same recommendation, which makes the tool predictable for study and review.

Tournament-first

Context matters

Stack depth, position, fold equity, antes, and ICM pressure are treated as part of the decision.

Inputs the heuristic considers

The engine scores the spot using broad poker factors rather than exact solver outputs. Each input nudges the recommendation toward aggression, caution, calling, or folding.

How confidence works

Confidence is not a probability that the action will win. It is a measure of how clearly the heuristic score clears or misses the internal threshold for the selected action. Close spots receive lower confidence. Obvious short-stack shoves, premium hands, and clear folds receive higher confidence.

How ICM pressure is estimated

The MVP uses simple tournament-stage adjustments. Bubble pressure and final-table spots penalize marginal gambles because bustout risk and pay jumps matter more. Early, middle, and heads-up stages apply different pressure assumptions.

When not to trust it blindly

Use JustShove as a study aid, not as guaranteed advice. Real decisions can depend on opponent tendencies, payout structure, blind format, stack distribution, rake, future game considerations, and exact ranges. The tool is designed to make tournament concepts easier to review, not to replace judgment.

What can improve later

The architecture keeps the heuristic layer isolated so stronger strategy sources can be added over time.

  • Precomputed Nash and push-fold charts
  • Solver API integrations for selected spots
  • Range-vs-range equity estimates
  • AI-assisted explanations that cite the underlying recommendation inputs