Preparing the requested workspace.
Build sharper slates. Control projections, exposure, team construction, and up to 150 unique lineups from one focused workspace.
SlateSnipe began with a simple question: What patterns in NFL fantasy scoring are conventional projection models underweighting—or missing entirely?
Over the last three years, I studied NFL fantasy data and identified variables and matchup-specific patterns that I wanted to test more thoroughly. Modern analytical tools and AI-assisted research allowed me to accelerate that testing while keeping every assumption, formula, and model decision under human direction.
That selective approach is central to our philosophy. A marksman accounts for wind only when it can meaningfully affect the shot. SlateSnipe applies the same principle to NFL data: situational variables influence a projection only when the evidence supports doing so.
No model replaces experience, so SlateSnipe puts meaningful control in your hands. Adjust projections, set player and Captain/MVP exposures, exclude players, and choose how heavily recent performance should influence your analysis. SlateSnipe provides the analytical foundation; you provide the judgment developed through experience.
SlateSnipe supports NFL single-game and multigame contests on FanDuel and DraftKings, with lineup controls tailored to each contest format.
This is only the beginning. Share the features and improvements that would make SlateSnipe more valuable through our Discord.
The future of DFS modeling is here. Take aim.FanDuel and DraftKings contest files are validated once, then made selectable for members. No player-pool CSV is needed from members. For FanDuel Edit Entries exports, select your own entry template; it stays in your browser.
Override projections, lock exposure targets, tune data recency, and filter single-game roster constructions.
Select the lineups you want and create a platform-specific CSV for your contest workflow.
Question approved projections, recent usage, matchup context, weather, and contest rules without exposing or altering the private model.
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