College Basketball

Campbell vs UNC Wilmington Prediction

March 8, 2026

10,000 Monte Carlo simulations

FINAL: UNC Wilmington 70 — Campbell 85. Our Monte Carlo simulation projected UNC Wilmington 83.2 - Campbell 79.3 (UNC Wilmington at 58.2% win probability). The spread is -7.0 and the total is 148.5.

UNC Wilmington
83.2
Projected Score
VS O/U 148.5
Campbell
79.3
Projected Score
Win Probability
58.2%
41.8%
UNC WilmingtonCampbell
-7.0
Spread (UNC Wilmington)
148.5
Total Line
10,000
Simulations
Calibrated accuracy at this confidence: 59.7% (4,284 games)

Projected Points Range 10th – 90th percentile

Campbell
667992
UNC Wilmington
708396
FINALUNC Wilmington 70 — Campbell 85
Projected
UNC Wilmington 83.2 — Campbell 79.3
Actual
UNC Wilmington 70 — Campbell 85

Pick Results

Over 149.0totalWIN+0.45u

Spread Analysis

UNC Wilmington Cover
37.1%
Campbell Cover
62.9%
ATS Edge: -5.2 pts

AI Intelligence Analysis

NEUTRAL -1GREEN ZONE64.8% WR (n=199)
Model sees UNC Wilmington winning by only 1.8 pts vs market -7.0, but UNC Wilmington ML has no odds listed and the 5.2pt spread gap cannot be monetized without ML pricing.

Key Factors

  • Model spreads UNC Wilmington winning by 1.8 pts vs market -7.0 — 5.2pt spread gap is notable
  • 58.2% UNC Wilmington win prob — moderate but no ML odds available to price the bet
  • Totals: model 162.6 vs market 149.0 — 13.6pt over signal but totals disabled
  • No ML odds listed (null) — cannot calculate market-implied probability

Risk Factors

  • 5.2pt spread gap is significant but model margin is noise — the market's -7 likely reflects superior scouting data
  • No ML odds to bet — the only actionable market is spread, which is our worst bet type (Grade D)
MODEL MARKET CONFLICTCONFERENCE GAME

Edge Analysis

Moneyline
UNC Wilmington 58.2%
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Spread
-7.0
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Total
148.5
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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 →

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