What Goes Into a Rithmm NFL Model?

People sometimes assume sports-betting AI models are built from simple box-score stats — things like “yards per game” or “completion percentage.”  
In reality, our NFL models process millions of opponent- and situation-adjusted data points every week.

We’re not modeling numbers in isolation.  
We’re modeling football in context.

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A Look Under the Hood

Here’s a small sample of just 10 inputs that feed our NFL predictive models.  
Each is derived from deeper sub-metrics and adjusted for opponent strength, game situation, and tempo.

1. Opponent-Adjusted Quarterback Tendencies
  – Run vs. throw tendency, adjusted for opponent strength and in-game situation.

2. Opponent-Adjusted Quarterback Efficiency Metrics
  – QB expected points added (EPA), recalibrated for defensive difficulty and context.

3. Game-Situation–Adjusted Running Back Explosiveness Index  
  – Big-run potential, conditional on down, distance, and game state.

4. Game-Situation–Adjusted Wide Receiver Explosiveness Index 
  – Explosive play probability relative to coverage type and opponent strength.

5. Opponent + Situation–Adjusted Offensive Tempo Index
  – Pace of play calibrated to opponent tendencies and score differential.

6. Quarterback-Adjusted EPA (Passing)
  – Expected points added per pass attempt, weighted by pressure and matchup.

7. Quarterback-Adjusted EPA (Rushing)
  – Expected points added per team run, adjusted for down, distance, and defense.

8. Pressure + Difficulty–Weighted Kicking Efficiency Score
  – Field-goal success probability incorporating distance, pressure, and environment.

9. Opponent-Adjusted Defensive EPA (Passing)
  – A defense’s true ability to limit positive pass plays, normalized by offensive strength.

10. Opponent-Adjusted Defensive EPA (Rushing)
   – Same methodology applied to rush defense performance.

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Why It Matters

Each metric on its own is informative.  
But when hundreds of these interact through our neural-network architecture, the model starts identifying patterns that traditional handicappers can’t see — tempo mismatches, pressure thresholds, and efficiency deltas that decide outcomes long before kickoff.

That’s why our NFL models consistently find value even against the most efficient markets.  
We’re not guessing — we’re quantifying football reality.

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From Data to Decision

Every prediction users see in the Rithmm app — spreads, totals, props — originates from this ecosystem of contextual data.  
We backtest across three full seasons to fine-tune weightings and ensure long-term stability.  
The result is an AI system that evolves with the game instead of chasing yesterday’s trends.

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Build Your Own Model

Inside Rithmm, you can experiment with these same inputs — emphasizing what matters most to you, whether it’s offense, defense, or pace — and instantly backtest your model’s performance.  

See what goes into smarter predictions.

Try Rithmm’s AI Model Builder!

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