Kim, Tom vs Homa, Max prediction for June 9, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects Homa, Max 0 - Kim, Tom 93. Kim, Tom is favored with a 59.0% win probability. The spread is -0.12.
Homa, Max
+0.00
Strokes Gained / Round
VS
H2H • RBC Canadian Open
Kim, Tom
+0.22
Strokes Gained / Round
Head-to-Head Win Probability
Homa, MaxKim, Tom
-105
Best Odds
+15.2%
Edge
1.5u HIGH
Sizing
Tournament Context
Event
RBC Canadian Open
Course
TPC Toronto at Osprey Valley (North Course)
Field
147 players
Player Profile — Kim, Tom
Strokes Gained
+0.22/round
Tour Avg
Course Fit
neutral
+0.000 SG adj
Expected Finish
93th / 147
Matchup Analysis
Kim, Tom
+0.22 SG
EF 93th
Skill Gap
-0.12 SG/round
tight edge for Homa, Max
Homa, Max
+0.00 SG
EF 0th · Tour Avg
Edge Breakdown
Our Model
59.0%
Books Say
51.2%
Edge
+15.2%
Kim, Tom vs Homa, Max: Model gives Kim, Tom 59.0% win probability vs 51.2% implied (+15.2% edge). Skill advantage: -0.12 SG/round. Expected finish: 93.
AI Intelligence Analysis
NEUTRAL +0YELLOW ZONE0.6% WR (n=201)
Model gives Kim 58.6% with -0.118 SG/round disadvantage and nearly identical expected finishes (93.5 vs 93.8); edge is margin noise, not signal—SKIP.
Key Factors
- Skill gap: -0.118 SG/round (Homa better)
- Expected finishes: 93.5 vs 93.8 (0.3-point difference, statistical noise)
- Model probability: 58.6% for weaker player (contradicts fundamentals)
- Edge percentage: +14.4%, but lacks foundation in skill or finish data
Risk Factors
- Expected finish gap is negligible (0.3 pts); model's 5.1% win-prob edge rests on distribution noise
- Tight odds (-105) mean vig is significant; model needs 51%+ to profit; small edge buffer
- High variance at 50/50 matchup; even 58% favorite loses 42% of the time
COIN FLIPFINISH NOISELOW CONFIDENCE
Edge Analysis
Moneyline
Kim, Tom 59.0%
+15.2 pts
Spread
-0.1
+15.2 pts
How this prediction was generated: This page shows output from the Olympus Bets PGA Tour Golf 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. Probabilities are calibrated using Bayesian methods and sized via the Kelly Criterion. Full methodology →