Kim, Si Woo vs Rose, Justin prediction for June 16, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects Rose, Justin 35 - Kim, Si Woo 61. Kim, Si Woo is favored with a 66.6% win probability. The spread is 0.59.
Rose, Justin
+1.20
Strokes Gained / Round
VS
H2H • U.S. Open
Kim, Si Woo
+1.70
Strokes Gained / Round
Head-to-Head Win Probability
Rose, JustinKim, Si Woo
-150
Best Odds
+11.0%
Edge
1.5u HIGH
Sizing
Projected Points Range 10th – 90th percentile
Kim, Si Woo
546168
Rose, Justin
283542
Tournament Context
Event
U.S. Open
Course
Shinnecock Hills Golf Club
Field
156 players
Player Profile — Kim, Si Woo
Strokes Gained
+1.70/round
World Class
Course Fit
good
+0.199 SG adj
Expected Finish
61th / 156
Matchup Analysis
Kim, Si Woo
+1.70 SG
EF 61th
Skill Gap
+0.59 SG/round
meaningful edge for Kim, Si Woo
Rose, Justin
+1.20 SG
EF 35th · Tour Elite
Edge Breakdown
Our Model
66.6%
Books Say
60.0%
Edge
+11.0%
Kim, Si Woo vs Rose, Justin: Model gives Kim, Si Woo 66.6% win probability vs 60.0% implied (+11.0% edge). Skill advantage: +0.59 SG/round. Expected finish: 61.
AI Intelligence Analysis
STRONG BET +1
Kim's strong +0.595 SG skill advantage and +0.199 course fit combine to generate +5.9% edge despite modest pricing (-165); expected finish gap (61 vs 83) confirms skill dominance.
Key Factors
- Strong skill differential: +0.595 SG/round (excellent advantage)
- Course fit: Kim +0.199 vs Rose +0.104 (modest Kim advantage)
- Expected finish gap: 22 strokes (61 vs 83) indicates major skill advantage
- 5.9% edge is conservative given 22-stroke expected finish gap
Risk Factors
- Kim expected finish of 61 is mid-tier (vs. elite 29-45 range); variance higher than top-player matchups
- Odds at -165 reflect market confidence; less value than lower-confidence edges
STRONG SKILL GAPEXPECTED FINISH SUPPORTSQUALITY PLAYER ADVANTAGE
Edge Analysis
Moneyline
Kim, Si Woo 66.6%
+11.0 pts
Spread
+0.6
+11.0 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 →