FINAL: Richmond 67 — Loyola Chicago 75. Our Monte Carlo simulation projected Richmond 73.0 - Loyola Chicago 61.6 (Richmond at 78.1% win probability). The spread is -4.5 and the total is 138.5.
Richmond
73.0
Projected Score
VS
O/U 138.5
Loyola Chicago
61.6
Projected Score
Win Probability
RichmondLoyola Chicago
-4.5
Spread (Richmond)
138.5
Total Line
10,000
Simulations
Loyola ChicagoRichmond L4
Calibrated accuracy at this confidence: 92.2% (4,284 games)
Projected Points Range 10th – 90th percentile
Loyola Chicago
486275
Richmond
597387
Projected
Richmond 73.0 — Loyola Chicago 61.6
Actual
Richmond 67 — Loyola Chicago 75
Pick Results
Richmond -5.0spreadLOSS-0.50u
Spread Analysis
Richmond Cover
67.9%
Loyola Chicago Cover
32.1%
ATS Edge: +10.9 pts
AI Intelligence Analysis
LEANGREEN ZONE78.0% WR (n=223)
Richmond projects at 78.0% home win probability vs Loyola Chicago with a 10.7-pt spread edge over the market (-5.0) — technically a HIGH EDGE WARNING territory, but the directional call is sound and the GREEN zone is strong; risk-reward on ML is the only viable angle.
Key Factors
- Model win prob 78.0% for Richmond — GREEN zone (CBB|ml|home|favorite|5-10%|any = 78.0% WR, n=223)
- Model spread -15.7 vs market -5.0 = 10.7 pt gap — HIGH EDGE WARNING (model likely overestimates margin)
- Richmond 78% win prob is directionally strong even if the margin is noise — ML is the play, not spread
- No ML odds available — lean is directional only
Risk Factors
- HIGH EDGE WARNING: 10.7 pt model-market spread gap — 30-day high-edge (25%+) WR is only 30.0% (3-7)
- No ML odds available — cannot compute Kelly or market-implied probability
- A-10 conference tournament — both teams know each other; market likely has solid information at -5
GREEN ZONEML VALUECONFERENCE GAMEHIGH EDGE WARNING
Edge Analysis
Moneyline
Richmond 78.1%
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Spread
-4.5
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Total
138.5
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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 →