Research & engineering · github.com/simcc-games
SIMCC Games
Simulation studies that give SIMCC evidence when it rules on open questions and designs new games.
Every study plays thousands of seeded games through the real Maths Warriors engine, never a reimplementation from the rulebook. Rule alternatives run in a Python port of the engine, which a parity suite checks against 3,000 golden games move for move. Studies that compare alternatives end with neutral evidence for SIMCC’s ruling and never recommend a rule.
- 8
- studies
- 3,000
- golden games for parity
- CC BY 4.0
- reports and CSVs
4,000 games per tier · Wilson 95% intervals · study 3
The gaps shrink up the ladder: 214 points from easy to medium, then 76, then 42. A player who always attacks the biggest die it can reach is as strong as medium.
Bradley–Terry fit · 1,000 games per tier pairing, 600 per heuristic · weakest anchored at 1000 · study 6
The first-player advantage turned out to be a race: almost every turn is a capture, so the first player reaches six captures first unless they are forced to skip. In games with no skip, the first player won every time. Whether a player can capture at all depends on how many dice they have left:
Mind attacks combine two or three dice with + − × ÷. With two dice they reach 13% of targets; with all six, 98%.
20,000 random boards per dice count · study 2
All eight studies
- Dice distributionsWhat do the six dice and a whole board produce?
- ReachabilityWhich targets can a set of dice capture?
- First-player advantageDoes moving first help, and what would the open rule questions change?
- Game length and tempoHow long are games, and does an early lead snowball?
- The timeout ruleHow often does the clock decide games?
- AI tier calibrationAre the AI tiers evenly spaced, and is the top tier safe from a trivial heuristic?
- Puzzle bank qualityIs the trainer bank balanced and free of repeats?
- The four new gamesAre the new generators solvable, separated by band, and deep enough?