How Our AI Models Work
Algobet builds sport-specific predictive systems using machine learning and data modeling — each tailored to uncover inefficiencies the betting market overlooks. Below, we detail how our NFL and Tennis models approach prediction and edge detection differently.
NFL Passing Yards Prop Models
Our NFL prop system uses two complementary machine learning models to predict player passing yards with both precision and realism. Each is trained on historical player-level data, incorporating opponent defenses, pace, pass rate, and weather conditions.
0.92 Model
Random Split- • Trained using random split of historical data
- • Achieves R² ≈ 0.92 (92% variance explained)
- • Excellent for raw predictive accuracy
- • Slightly optimistic due to data mixing
0.78 Model
Time-Aware- • Uses strict time-based training split
- • Training: 2022–2024 + 2025 Weeks 1–4
- • Testing: 2024 + 2025 Weeks 1–4
- • R² ≈ 0.78, more realistic for live betting
Why Use Both Models?
By running both models in parallel, we maximize confidence and minimize variance in our betting recommendations:
- • Agreement bets: When both models agree → higher-confidence Over/Under plays
- • Disagreement bets: When models diverge → marked as “Skip” due to uncertainty
- • Complementary strengths: The 0.92 model offers precision, while the 0.78 model grounds predictions in out-of-sample realism
Edge-Based Confidence
After predicting player performance, we compare model projections to sportsbook lines. The greater the difference (“edge”), the higher the confidence category:
Skip
Edge < 10 yards
Low confidence
Weak
Edge 10–20 yards
Moderate confidence
Strong
Edge ≥ 20 yards
High confidence
Example
- • Prediction: 265 yards
- • Sportsbook line: 240.5 yards
- • Edge: +24.5 → Over (Strong)
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