Speaking our language what the numbers mean
- Leverage
- Ceiling ÷ projected ownership. High means a big outcome almost nobody has. It is not a rating. A 6.0 on a player who never hits is still a loss.
- Ownership
- The percentage of the whole field we project to roster a player. This is the number nobody competes with Vegas to price, which is why it is the product.
- Field overlap
- How many of your 9 players sit in the average field lineup. This one is exact, not modelled: it is just the sum of your players' ownership. Low means you are somewhere the field is not.
- Chalk
- A heavily owned player. Usually chalk for a reason. Fading it just to be different is how people lose money feeling clever.
- Ceiling
- Roughly a 90th percentile outcome, not a best case. It scales with position: a WR's range is far wider than a QB's.
- Constraint check
- Every lineup has one QB, so QB ownership across the field must sum to 100%. We print it live. If our model breaks, you see it before we do.
- Leverage tilt
- How hard the optimizer leans toward plays the field is off. At 0 you build straight off projections and correct chalk stays in.
- Stack
- A QB plus his own pass catchers. When he throws a touchdown, they catch it, so their scores rise together. That shared upside is the point.
- Max exposure
- The most lineups any single player can appear in. Caps how badly one wrong call can hurt across a whole set.
- Win %
- From the simulation: how often this lineup beats the modelled field. It disagrees with raw projection more than you would think.
- ROI
- Use it to rank lineups against each other, never as a forecast. Our simulated field is more random than real opponents, so it runs optimistic.
Leverage = ceiling ÷ projected ownership. Points are a commodity. Vegas prices them and
everyone sells projections. Nobody prices what the field will do. This tells you where
the field is concentrated, not what to fade: plenty of chalk is chalk because it's the
right play. The point is to choose knowingly instead of guessing.
Use your own slate
Drop DKSalaries.csv here, or bring your own projections and we'll do the ownership.
| Pos | Player | Salary | Proj | Ceil | Own% | Lev | Val | Lock/Fade |
|---|
Who moves together. A QB and his receivers score on the same plays, so their
outcomes rise and fall as one. But not equally: the alpha who commands 60% of the
targets is nearly the offense, while the WR4 barely registers. That gap is the
difference between a stack that wins and one that just costs salary. Correlation
here scales with target share and with the game total, so a shootout and a slog do
not look alike. Two players in different games do not interact at all, which is
why this is grouped by game.
These are reasoned relationships, not ones fitted from
historical game logs. Trust the ranking, not the third decimal.
Monte Carlo contest simulator. We simulate the slate thousands of times with
correlated outcomes, because when a QB throws for 400 his receivers eat too. Then we
simulate a field of opponents drawn from our ownership model and score everyone on the same
outcomes.
Build lineups first
Hit Optimize Lineups, then come back. The sim ranks your lineups by win probability instead of mean projection, and the order changes.
No lineups yet
Set your rules and hit Optimize Lineups. Turn up Leverage to build around the plays the field is off.