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SIMSLATE// observatory
SimSlate Lab · plate 02 — the simulator

EVERY SUNDAY,
FIFTY THOUSAND
TIMES.

A projection is one guess. A distribution is every guess, weighted. Each streak below is one simulated world falling into place — the histogram isn't drawn, it's earned, one Sunday at a time.

Worlds simulated
000,000
running mean · p99 flares 0
01

THE MEAN IS EARNED SLOWLY. THE TAIL IS EARNED NEVER.

The law of large numbers is patient: after a few hundred worlds the running mean still swings like a compass near a magnet; after fifty thousand it has settled to within a rounding error of the projection. That's why SimSlate never trusts a small sample — a thousand sims will lie to you about a player's mean, and lie worse about his ceiling.

But watch the p99 line. It never settles the way the mean does — tail estimates converge at a fraction of the rate, because worlds that far out arrive once in a hundred Sundays. The rarer the event, the more worlds you owe it. Fifty thousand is the rent we pay to see the tail clearly.

Running estimate vs. worlds simulatedn = 0
02

WORLDS FALL IN PAIRS — THE COPULA TETHER.

QB × WR1 · same world, tethered drawGaussian copula
ρ = 0 ρ = .9 ρ = 0.62

A quarterback and his receiver do not live in separate universes. When the passing game erupts, both erupt — one touchdown is two stat lines. SimSlate draws every player's world from a Gaussian copula: correlated normal scores under the hood, each mapped back onto its own fitted marginal so no player ever loses his personal distribution.

Drag the tether. At ρ = 0 the pair rains independently — a great QB world says nothing about the WR. At ρ = 0.62 (a real same-team stack value) the streaks start falling together: ceilings arrive in bundles. That bundle is why stacking wins tournaments — you're not betting on two events, you're betting on one.

03

THE INSTRUMENT.

Observatory hardware, as flown in production. Nothing on this page is a mockup of the engine — the rain above runs the same inverse-CDF sampling the real simulator uses.

Worlds per contest
50,000
Correlated Monte-Carlo draws, batched and vectorized.
Marginals
Shifted lognormal
Right-skewed, fit to mean, sd, ceiling and Boom/Bust probabilities.
Dependence
Gaussian copula
Team stacks, bring-backs, QB↔DST negatives — kept positive-semi-definite.
Field
10,000 lineups
Ownership-weighted entrants, exposure-calibrated to the slate.
Ranking
ROI, not points
Every lineup priced against a top-heavy GPP payout curve with duplication.
Verdict cadence
~2 min
Full 50k × 50k contest sim on the compute tier.