FINAL: UC Davis 79 — UC Santa Barbara 73. Our Monte Carlo simulation projected UC Davis 79.1 - UC Santa Barbara 80.6 (UC Davis at 52.9% win probability). The spread is 4.5 and the total is 139.5.
UC Davis
79.1
Projected Score
VS
O/U 139.5
UC Santa Barbara
80.6
Projected Score
Win Probability
UC DavisUC Santa Barbara
+4.5
Spread (UC Davis)
139.5
Total Line
10,000
Simulations
Calibrated accuracy at this confidence: 59.1% (4,284 games)
Projected Points Range 10th – 90th percentile
UC Santa Barbara
688194
UC Davis
667992
Projected
UC Davis 79.1 — UC Santa Barbara 80.6
Actual
UC Davis 79 — UC Santa Barbara 73
Pick Results
Over 142.5totalWIN+1.36u
Spread Analysis
UC Davis Cover
63.6%
UC Santa Barbara Cover
36.4%
ATS Edge: +5.1 pts
AI Intelligence Analysis
NEUTRAL -1RED ZONE22.8% WR (n=79)
The model projects UC Davis (home) as a tiny 54.3% win favorite despite the market making UCSB the -5.5 away favorite — the model directly contradicts the market direction, and the home underdog zone is deeply RED (22.8% WR), making this a hard skip.
Key Factors
- Market has UCSB as -5.5 away favorite; model gives UC Davis 54.3% home win — direct directional contradiction
- Home underdog zone is deeply RED (22.8% WR, n=79, z=-4.84) — this is a money-losing profile
- No ML odds available — cannot evaluate either side's market-implied probability
- Model-market spread disagreement: market says UCSB -5.5, model says UC Davis -1.0 — 6.5 pt gap near HIGH EDGE WARNING
Risk Factors
- Home underdog RED zone: 22.8% WR — statistically significant money pit
- Large model-market directional conflict — DEFAULT hypothesis is model is wrong, not market
- No ML odds data to evaluate this contradiction further
MODEL MARKET CONFLICTCONFERENCE GAME
Edge Analysis
Moneyline
UC Davis 52.9%
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Spread
+4.5
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Total
139.5
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How this prediction was generated: This page shows output from the Olympus Bets College Basketball Monte Carlo engine. Each game is simulated 10,000 times using real-time team data, injury reports, and current odds. Probabilities are calibrated using Bayesian methods and sized via the Kelly Criterion. Full methodology →