Inspect player ratings and matchup evidence, compare your options, and manage your manual roster.
Professional provenance
The fantasy-football expression of real sports decision work.
The team behind FantasyOmatic has also built systems for recruiting rooms, coaching staffs, and player-development workflows. The common thread is turning complicated sports data into a decision someone can understand and act on.
That is what separates FantasyOmatic from an AI content site: the ratings come from a fitted model and a long-running sports analytics practice. AI is the interface to the evidence, not the source of it.
FootballRecruiting and team-operations systems
BasketballCoach-facing performance analytics
BaseballPlayer-development decision tools
FantasyOmatic does not use confidential team data or proprietary club models. It shares the same team, decision-system discipline, and standard: analytics should end in a clear action.
Published ratings are calculated by statistical models using verified football data.
How are the offensive ratings calculated?
Ridge regression fits statistical relationships in verified football performance data. Versioned calibration converts each native offensive output to its public rating. Offensive Player Rating, This Week, ROS and Playoffs describe different forecast horizons. Context changes native forecasts before the published Rating100 conversion. Kicker and D/ST models include schedule and market inputs and use within-position scales. Expert consensus is a comparison benchmark where verified.
Are the ratings copied from expert consensus?
The offensive model calculates its own outputs. Dated expert consensus can be used as a comparison benchmark. Kicker and D/ST models separately use schedule and market inputs; they are not described as market-independent.
What does historical bootstrap mean?
Before qualifying current-season evidence exists, the published release can use verified historical player-model outputs and current roster, schedule and availability inputs. A provisional floor of 40 is a rule for insufficient evidence, not earned performance. The release provenance identifies the actual source seasons.
How is model quality evaluated?
Historical walk-forward tests train on eligible earlier evidence and evaluate later outcomes. Validation reports identify the model version, seasons, sample size and error metric; historical results do not guarantee future accuracy.
