Prediction Confidence Scores

Prediction Confidence Scores

Confidence scores help you understand how certain our model is about each prediction.

What is a Confidence Score?

A confidence score represents how strongly our ensemble of models agrees on a prediction:

• High confidence: Multiple models agree strongly
• Low confidence: Models disagree or show uncertainty
• Not the same as win probability: Confidence is about certainty, probability is about likelihood

Confidence Tiers

High Confidence (70-100%)

Characteristics:

• All model components agree
• Clear statistical advantage for one team
• Strong historical precedent
• Multiple factors align

What it means:

• Model is very certain about outcome
• Data strongly supports prediction
• Lower likelihood of surprise

How to use:

• Safe for combining in parlays
• Good for same-game parlays
• Suitable for larger wagers (if betting)
• High probability games to watch if you want guaranteed action

Medium Confidence (55-69%)

Characteristics:

• Models mostly agree
• Clear favorite exists
• Some contradictory indicators
• Moderate statistical edge

What it means:

• Model leans one direction
• Upset is possible
• Some uncertainty remains

How to use:

• Do additional research
• Check recent news
• Verify with other sources
• Smaller position sizes if betting

Low Confidence (<55%)

Characteristics:

• Models disagree
• Very evenly matched teams
• Conflicting data points
• Historical uncertainty

What it means:

• True toss-up game
• Coin flip territory
• High upset potential

How to use:

• Great games to watch (competitive)
• Risky for betting
• Perfect for prop bets instead of sides
• Consider staying away if betting

How Confidence is Calculated

Our confidence score combines:

1. Model Agreement (40%): How much do XGBoost, Random Forest, and Neural Net agree?
2. Feature Strength (30%): How strong are the predictive signals?
3. Historical Similarity (20%): How well have similar games been predicted?
4. Data Quality (10%): How complete and reliable is input data?

Confidence vs. Win Probability

These are different metrics:

Confidence by Game Type

High Confidence Games Tend To Be:

• Ranked vs. unranked non-conference
• Power 5 vs. Group of 5
• Top 10 team vs. bottom 25 team
• Teams with 4+ win differential

Low Confidence Games Tend To Be:

• Rivalry games (intangibles matter)
• Evenly ranked teams
• Conference championship games
• Teams with identical records
• Early season (less data)

Confidence Trends Through Season

Confidence scores change as season progresses:

• Week 1-3: Lower average confidence (limited data)
• Week 4-10: Highest average confidence (enough data, teams stable)
• Week 11-13: Moderate confidence (teams evolving)
• Bowls: Lower confidence (opt-outs, long break)

Using Confidence for Betting

Bankroll Management by Confidence

• High Confidence: 2-3% of bankroll
• Medium Confidence: 1-1.5% of bankroll
• Low Confidence: 0.5% or avoid

Confidence-Based Strategies

• High Confidence Parlays: Combine 2-3 high-confidence favorites
• Medium Confidence Singles: Straight bets only
• Low Confidence Avoid: Or bet underdog for value

Confidence Warnings

We display warnings when:

• High spread, low confidence: Potential upset brewing
• Injury uncertainty: Key player status unknown
• Weather concerns: Extreme conditions expected
• Limited data: New teams or unusual matchups
• Line movement: Prediction and line moving opposite directions

Historical Confidence Performance

Our confidence scores have proven reliable:

• High Confidence Games: 78.4% prediction accuracy
• Medium Confidence Games: 68.2% prediction accuracy
• Low Confidence Games: 53.1% prediction accuracy (basically coin flips)

When Confidence is Misleading

Confidence scores can be less reliable:

• Late-breaking injuries: Not reflected in model
• Weather changes: Predictions made before forecast update
• Motivational factors: Hard to quantify in models
• Coaching changes: Mid-season staff changes
• Opt-outs: Bowl game absences

Tips for Using Confidence

1. Combine with win probability: Both should align for best bets
2. < strong>Check timestamp: When was prediction last updated?
3. Read analysis: Understand what drives confidence
4. Track your own data: Do high-confidence predictions work for you?
5. Don't force action: It's okay to pass on low-confidence games
6. Value confidence differences: When we're confident and public isn't, edge exists