NFL · DraftKings
An NFL lineup optimizer built around game correlation, not just team correlation.
Our cross-sport research found that in NFL, the unit that separates winners is the game, not the team: QBs in the slate's highest-scoring game and bring-back correlation showed up disproportionately in top finishers. The optimizer builds toward that.
How it builds a lineup
QB + pass-catcher stacks
The optimizer pairs your QB with same-team pass-catchers as a core building block, in line with how top-100 finishers are actually constructed on real DraftKings slates.
Bring-backs
Opponent-side correlation (a "bring-back" player from the QB's opposing team) is built in as a first-class construction option, not an afterthought — it shows up far more often in winning lineups than in the median field lineup.
Game-environment allocation
Roster construction is allocated toward the slate's highest-scoring game environments, using the same game-level correlation logic our research found separates winners from the field.
Ceiling objective
GPP builds optimize against a quantile ceiling model, not the mean projection — tournaments are won on upside, and the objective function reflects that.
No forced correlation that doesn't pay
We tested forcing a same-team RB alongside the QB and found no meaningful edge in our data — so the optimizer doesn't force it as a default construction rule.
Classic-format focus
Full-slate classic contests produce far more unique winning lineups than single-game showdown formats in our data — the optimizer is built for multi-entry classic play.
Why this approach
MLB and NFL reward different correlation structures. In MLB you stack a team, because the batting order scores together. In NFL, our study of a 277,000-entry DraftKings Classic contest found the correlated unit is the game: QBs in the slate's highest-scoring game and bring-back players were dramatically overrepresented among top finishers relative to the field. The optimizer's NFL logic is built around game selection first, stack shape second.
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