Running backs · 2013
San Diego
The public metric pointed in the opposite direction.
- Traditional
- Top-12 toughest by FPA
- Adjusted model
- 11th-easiest matchup
Opponent-Adjusted Machine Learning
Fantasy Football
Ratings Calculated
From Football Data
Fantasy Points Per Game and Fantasy Points Against don't adjust for opponent strength. FantasyOmatic uses statistical Ridge models to separate player ability from supported opponent context. Ratings are calculated from football data, not generated by a chatbot. AI Coach and Podcast explain published results.
1 NFL season modeled · 310 player-games · CBS-style non-PPR
Truth Engine
Raw stats hide the truth
Opponent quality changes what every box score means. Replay four moments when the public metric and the adjusted model reached materially different conclusions.
“Pittsburgh's pass defense is bottom five in net yards per attempt, but they've faced the murderer's row of Justin Fields, Sam Darnold, and Drake Maye.”
Pittsburgh looks terrible on paper. But they faced weak QBs. New England's defense looks even worse — yet they faced Geno Smith, Tua, and Aaron Rodgers, and still held Rodgers to 139 yards on 23 attempts. The raw numbers tell opposite stories from reality.
What Sigmund does manually, FantasyOmatic's algorithm does systematically — for every defense, every player, every week, automatically.
Running backs · 2014
New Orleans
Opponent strength revealed danger hidden in the middle.
- Traditional
- Middle of the pack by FPA
- Adjusted model
- 6th-toughest matchup
Quarterbacks · 2013
Matthew Stafford
A soft schedule had inflated the raw ranking.
- Traditional
- 7th by raw FPPG
- Adjusted model
- Just inside the top 20
Quarterbacks · 2013
Tony Romo
The production was more opponent-dependent than it looked.
- Traditional
- 10th by raw FPPG
- Adjusted model
- 21st after adjustment
The model, now
D.Kincaid
Start with neutral talent. Then account for this opponent, this venue, this surface, and this injury context.
56.5 Player Rating → 80.2 This Week
Historical replay
San Diego
Traditional read
Top-12 toughest by FPA
Opponent-adjusted
11th-easiest matchup
The public metric pointed in the opposite direction.
Method
How one rating becomes a weekly decision
Player Rating describes opponent-neutral value. This Week uses supported weekly context and its own frozen conversion; it is not Player Rating plus an adjustment.
Player Rating
0–100
A neutral-context value score on one cross-position scale. Matchup and injury context stay separate.
Defense Impact
BY POSITION
How much a defense suppresses or elevates QBs, RBs, WRs, and TEs after controlling for opponent quality.
Week Context
MODEL INPUTS
Supported weekly model context, separate from projected points. It is not an additive adjustment to Player Rating.
Defense Impact is position-specific native model evidence where verified, not a standalone 0–100 defense score. Player Rating and This Week use separate frozen conversions; subtracting them does not isolate an opponent effect. Bootstrap releases have no current-season defensive evidence.
Explainer · 2 min
How the ratings get made
Why raw fantasy points against a defense mislead you, and how Player Rating, Defense Impact, and Week Context are built to answer the question those numbers cannot.
Figures in this video are illustrative examples, not live ratings. Player names and numbers are chosen to show how the scale behaves. For published ratings, see the current rankings. The narration is AI-generated; the on-screen text carries the same explanation.
What the explainer covers
Two defenses can allow identical fantasy points and still be nothing alike, because raw totals absorb the schedule that produced them. The video walks through the three numbers built to separate those effects: Player Rating, a neutral-context value score on one 0–100 cross-position scale; Defense Impact, the position-specific effect a defense has after opponent quality is controlled for; and weekly model context. In current releases, Player Rating and This Week use separate frozen conversions; their difference is not an additive adjustment to the player baseline.
Scoring calibration
The offensive native model is fitted to a CBS-style non-PPR target.Synced or manual league scoring is used only where a feature explicitly says it is league-aware. FantasyOmatic does not present an unsupported scoring selector for the universal Rating100 scale.
Three-week current-season calibration
Week 1 can use verified historical player-model evidence with current rosters, schedule and availability. Once qualifying regular-season evidence exists, the offensive Ridge model fits completed current-season player-games and weights recent weeks more heavily. The ratings stay in early-season calibration until a successful live run has incorporated three completed weeks. Before then, roles and usage have less live evidence and may move faster. This status follows incorporated data, not the calendar or the displayed projection week.
01Neutral talent
02Opponent
03Efficiency
04Venue + surface
05Injury context
Calculated from football data
Does ChatGPT generate FantasyOmatic ratings?
Our offensive ratings use statistical machine-learning models. AI Coach and Podcast use generative AI to explain published results and help you apply them; they do not calculate the underlying ratings.
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 Overall, This Week, ROS and Playoffs each use a separate frozen conversion of their native model output. Their difference does not isolate an opponent effect. 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.
Proof
One scale. Every position.
Most sites rank players within their position. We rate them on a single 0–100 scale that works across positions.
Illustrative value-scale example · not live player ratings
QB
92
QuarterbackWR
88
Wide receiverTE
78
Tight endOn FantasyOmatic's value scale, the illustrative QB at 92 carries more cross-position value than the WR at 88 after position-specific replacement baselines are applied.
Use Player Rating to compare value. Use This Week context and projected points for lineup decisions.
Track Record
A public operating history
Selected milestones are kept separate from the reproducible model test below. We do not use anecdotes as accuracy evidence.
- 2007
FantasyOmatic Model Work Begins
The opponent-adjustment system begins as an independent fantasy-football research project.
- 2014
NFL.com 'Number Crunch' Column
Weekly guest contributor to NFL.com/Fantasy, bringing algorithmic analysis to the mainstream.
- 2023
First Fantasy Sports ChatGPT Plugin
Pioneered conversational AI access to real-time algorithmic fantasy data.
- 2023
Academic Validation
Co-presented 'AI Interactions with Sports Data' at the Midwest Sports Analytics Meeting with Dr. Michael Schuckers.
- ONGOING
Professional Sports Decision Systems
The same team behind FantasyOmatic builds recruiting, coaching, performance-analysis, and player-development systems used across professional football, basketball, and baseball.
Industry Validation
Algorithm vs. opinions
FantasyOmatic starts with fitted model outputs, then translates those outputs into one cross-position 0–100 language.
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.
Editorial rankings
Human rankings and projection panels
Fitted model outputs, not a consensus vote
FantasyPros
Aggregates consensus from 100+ experts
Single algorithm, independently ranked among those experts
Common rankings
Position ranks and separate projections
Cross-position 0–100 value scale
Traditional Metrics
Fantasy Points Against (raw, unadjusted)
Opponent-adjusted defense impact by position
As Seen On
Trusted by the industry
NFL.comGuest Columnist2014–15
Sirius XMRadio HostFantasy Sports Radio
FantasyProsMost Accurate Expert2019, 2022
MSAMAcademic Presenter2023
Documentary
The work behind the numbers
A closer look at the question, process, and machine-learning system behind FantasyOmatic.
Feature documentary
Building a winning fantasy team on data, not luck
The story of how one matchup question grew into the machine-learning system behind FantasyOmatic's ratings.
Documentary transcript
The captioned film covers Chris Ippolite's path from a fantasy-football question to a matchup-adjusted model, the system's independent accuracy record, and the data workflow behind the product.
“We are a small team but making a big impact on the industry.”
Built in Green Bay by a football obsessive who found a flaw in the math everyone else was using. Model work began in 2007. The current ratings are fitted from the training seasons recorded on each pipeline run, while AI provides a conversational layer over those verified numbers.
1
NFL seasons modeled this run
3,241
walk-forward predictions
4.897
enhanced MAE
1.7%
lower MAE vs no efficiency feature
Audit the evidence for free. Add decision tools when you need them.
Player Rating and its methodology stay public. All-Pro and Hall of Famer are one-time 2026 Season Passes through Week 18.