FINAL: CHA 129 — IND 108. Our Monte Carlo simulation projected CHA 110.0 - IND 105.8 (CHA at 62.3% win probability). The spread is -16.0 and the total is 234.5.
CHA
110.0
Projected Score
VS
O/U 234.5
IND
105.8
Projected Score
Win Probability
CHAIND
-16.0
Spread (CHA)
234.5
Total Line
10,000
Simulations
INDCHA W5
Calibrated accuracy at this confidence: 60.2% (1,191 games)
Projected Points Range 10th – 90th percentile
IND
96106116
CHA
100110120
Projected
CHA 110.0 — IND 105.8
Actual
CHA 129 — IND 108
Pick Results
IND +16.0spreadLOSS-1.00u
Model Projection
MLELITE+870
IND ML
+27.4%
Edge
37.7%
Win/Cover Prob
1.0u
Units
100
Quality
Model gives IND 38% win prob
Against the Spread
IND ATS
-11.8 pts edge | 82% cover
ELITE
Over/Under
UNDER 234.5
-18.7 pts edge | 79% under
MARGINAL
Starting Lineups
IND9 OUT
Jalen Slawson5.7PPG4.4RPG2.7APG
Ben Sheppard7.1PPG3.0RPG1.7APG
Jay Huff9.4PPG3.8RPG1.4APG
Micah Potter9.2PPG4.4RPG1.4APG
Ethan Thompson5.8PPG1.8RPG1.5APG
CHA3 OUT
Kon Knueppel18.8PPG5.4RPG3.4APG
Miles Bridges17.2PPG5.8RPG3.2APG
Brandon Miller20.4PPG5.0RPG3.4APG
LaMelo Ball19.6PPG4.8RPG7.1APG
Coby White17.6PPG3.6RPG4.2APG
AI Intelligence Analysis
NEUTRALGREEN ZONE53.3% WR (n=156)
Market is correctly priced on CHA cover despite -16 spread being extreme; 13+ pt quality gap is real but covering a -16 as a heavy favorite is inherently difficult and offers no edge.
Key Factors
- CHA net rating +4.76 vs IND net rating -8.31 = 13.07 pt quality gap (top-20 vs bottom-5 team)
- IND starting lineup is G-League tier (Jalen Slawson 6p, Ben Sheppard 7p, Jay Huff 9p)
- CHA recent form L5: 3-2, L10: 7-3 (67% and 70% WR)
Risk Factors
- Covering -16 as a heavy favorite is inherently low-probability (8-12% realistically), even vs bad teams
- IND occasionally has individual hot performances despite bad record (L5 is 3-2)
- Model conservatively predicts CHA -8.3 (suggesting cover is hard)
QUALITY MISMATCHAWAY DOG POISON
Edge Analysis
Moneyline
CHA 62.3%
-11.8 pts
Spread
-16.0
-11.8 pts
Total
234.5
-18.7 pts
How this prediction was generated: This page shows output from the Olympus Bets NBA 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 →