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
The Numbers
Everyone Else Uses
Are Lying to You
Fantasy Points Per Game and Fantasy Points Against don't adjust for opponent strength. FantasyOmatic separates player ability from opponent context with a current-season, recency-weighted Ridge model. AI never creates the ratings; it only lets you question them.
Current-season model · 0 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
Weekly starter
Start with neutral talent. Then account for this opponent, this venue, this surface, and this injury context.
82.0 Player Rating → +6.0 Week Context → 88.0 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 establishes cross-position value. Defense Impact and Week Context explain how that baseline changes for one week.
Player Rating
0–100
What a player would score against a neutral defense. Talent isolated from circumstance.
Defense Impact
BY POSITION
How much a defense suppresses or elevates QBs, RBs, WRs, and TEs after controlling for opponent quality.
Week Context
+/−
The signed effect of this week's opponent and circumstances. Positive = favorable. Negative = danger.
Defense Impact is position-specific model evidence, not a standalone 0–100 defense score. Week Context translates that evidence and the rest of this week's circumstances into movement on the player rating scale.
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 hydration
Historical modeling remains active from the start. FantasyOmatic stays in early-season calibration until a successful live ratings run has incorporated three completed current-season weeks, which helps stabilize current roles and usage. Rookies and players in new situations have less current-season 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
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.
QB
92
Lamar JacksonWR
88
CeeDee LambTE
78
Sam LaPortaA QB rated 92 and a WR rated 88 are directly comparable. This makes trade analysis, flex decisions, and draft strategy quantitative instead of subjective.
Player Rating compares cross-position value. This Week context and projected points answer the separate lineup question.
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.
Industry Validation
Algorithm vs. opinions
FantasyOmatic starts with fitted model outputs, then translates those outputs into one cross-position 0–100 language.
ESPN / CBS / Yahoo
Expert human opinions and editorial panels
100% machine learning, no human bias
FantasyPros
Aggregates consensus from 100+ experts
Single algorithm, independently ranked among those experts
Every Platform
Position-only rankings (QB #5, WR #8)
Cross-position 0–100 scale for true comparisons
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.
Current-season
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.