NFL DFS Optimizer And Simulator

Load lineups and run a simulation to see results here.

Checked against 16 real slates — good for comparing strategies against each other, not a calibrated prediction of real payout. See the note at the bottom of this tab for what's verified vs. not.

Per-lineup results

Click a column header to sort. Click a row to see who's in that lineup.

#	Avg score	P10	P50	P90	Cash%	Top 1%	Top 0.1%	Avg ROI	Avg payout

ROI by lineup

Where the edge (or lack of it) is concentrated across your portfolio.

Portfolio (all lineups as combined entries in this contest)

Methodology & limitations — what this simulation does and doesn't capture

What this simulation does and doesn't capture: player outcomes are sampled from a correlated model (game/team-level shared shocks, not independent per player) anchored to each player's own median/floor/ceiling. The opposing field mixes a naive ownership-weighted "public" portion with a small precomputed pool of near-optimal "sharp" lineups (default 15% of the field, scored exactly each trial rather than folded into the public sample) — a deliberate improvement over pure noise, but the sharp fraction and correlation weights are reasoned placeholders, not fitted to real historical data. Each trial samples a subset of the public field, not the full field, and fits a statistical model to that sample's upper tail so your score can be placed continuously within it rather than by counting how many sampled entries you beat — that's what lets Top 1% and Top 0.1% resolve as distinct, roughly accurate numbers instead of both collapsing into "beat everyone in the sample." The further out you go past Top 0.1% (finishing-1st-type odds), the less this has been validated — that estimate has run consistently high in testing even after two rounds of fixes, so treat anything past Top 0.1% as a rough, likely-optimistic upper bound, not a real probability. Trust the aggregate rates across thousands of trials, not any one number in isolation.

Checked against 16 real slates (2023–2024 contest history, matched to old ETR exports): the simulated field's score shape held up well — when a slate's projections were accurate, the simulator's full percentile curve tracked the real one closely (within ~1 point at every percentile, in the best case). The remaining gap is mostly explained by ordinary week-to-week projection error, not a structural flaw in the correlation/variance model. Across all 16 slates there's a small, borderline-significant upward bias — the simulator ran about 5 points optimistic on average (95% CI roughly [0, +9.5]), with wide slate-to-slate swings (−8 to +25) that a sample this size can't fully explain away as either real or noise.

What that means for the numbers above: treat ROI, cash rate, and top-X% figures as comparative — reliable for judging one strategy or lineup against another on the same slate — not as calibrated predictions of real-money return. The absolute level may run slightly optimistic. Confidence is highest for cash-line/median-range outcomes (best-sampled bucket); the extreme tail (top 0.1%, top 0.01% — what actually separates GPP strategies) has the least real-world validation, since real ceiling outcomes are rare by nature and this history didn't produce enough of them to check against.

NFL DFS Optimizer And Simulator

Upload projections

Pick a strategy

Generate

Generated lineups

Portfolio exposure

Per-lineup results

ROI by lineup

Portfolio (all lineups as combined entries in this contest)

⭐ Finalists (0)

Finalists exposure

All batches (0 lineups)

NFL DFS Optimizer And Simulator

Upload projections

Pick a strategy

Generate

Generated lineups

Portfolio exposure

Per-lineup results

ROI by lineup

Portfolio (all lineups as combined entries in this contest)

⭐ Finalists (0)

Finalists exposure

All batches (0 lineups)

Match your file's columns

Match report