College Basketball

Wyoming vs UNLV Prediction

March 11, 2026

10,000 Monte Carlo simulations

FINAL: UNLV 73 — Wyoming 70. Our Monte Carlo simulation projected UNLV 92.8 - Wyoming 86.8 (UNLV at 58.7% win probability). The spread is -12.0 and the total is 154.0.

UNLV
92.8
Projected Score
VS O/U 154.0
Wyoming
86.8
Projected Score
Win Probability
58.7%
41.3%
UNLVWyoming
-12.0
Spread (UNLV)
154.0
Total Line
10,000
Simulations
Wyoming L4UNLV
Calibrated accuracy at this confidence: 59.7% (4,284 games)

Projected Points Range 10th – 90th percentile

Wyoming
7487100
UNLV
8093106
FINALUNLV 73 — Wyoming 70
Projected
UNLV 92.8 — Wyoming 86.8
Actual
UNLV 73 — Wyoming 70

Pick Results

Over 153.0totalLOSS-1.50u

Spread Analysis

UNLV Cover
46.7%
Wyoming Cover
53.3%
ATS Edge: -1.4 pts

AI Intelligence Analysis

LEANGREEN ZONE77.2% WR (n=79)
UNLV is a modest home favorite at -3.5 with the model projecting a 59.9% win probability — the HOME ML zone is GREEN and the model/market are reasonably aligned, making this a low-edge but directionally sound lean on UNLV.

Key Factors

  • Model win prob 59.9% for UNLV — GREEN zone CBB|ml|home|favorite|5-10%|60-65% = 77.2% WR (n=79)
  • Model spread -2.3 vs market -3.5 = only 1.2 pt gap — model and market largely agree on the result
  • Total discrepancy +25.2 pts (179.7 vs 154.5) — systematic model over-estimation, do not touch totals
  • No ML odds available — lean is tentative without pricing confirmation

Risk Factors

  • No ML odds data — cannot compute market-implied probability or Kelly value
  • Totals gap of +25.2 pts is another extreme outlier — model totals suspect today
  • Small model-market spread alignment difference means minimal actionable edge
GREEN ZONEML VALUECONFERENCE GAME

Edge Analysis

Moneyline
UNLV 58.7%
--
Spread
-12.0
--
Total
154.0
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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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