Im, Sungjae vs Rodgers, Patrick prediction for May 5, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects Rodgers, Patrick 45 - Im, Sungjae 33. Im, Sungjae is favored with a 59.6% win probability. The spread is -0.26.
Rodgers, Patrick
+0.31
Strokes Gained / Round
VS
H2H • Truist Championship
Im, Sungjae
+0.04
Strokes Gained / Round
Head-to-Head Win Probability
Rodgers, PatrickIm, Sungjae
-125
Best Odds
+7.2%
Edge
1.0u MEDIUM
Sizing
Projected Points Range 10th – 90th percentile
Im, Sungjae
263340
Rodgers, Patrick
384552
Tournament Context
Event
Truist Championship
Course
Quail Hollow Club
Field
72 players
Wind
12 mph
Temp
76°F
Conditions
harder (+0.6)
Player Profile — Im, Sungjae
Strokes Gained
+0.04/round
Tour Avg
Course Fit
excellent
+1.129 SG adj
Expected Finish
33th / 72
Matchup Analysis
Im, Sungjae
+0.04 SG
EF 33th
Skill Gap
-0.26 SG/round
tight edge for Rodgers, Patrick
Rodgers, Patrick
+0.31 SG
EF 45th · Tour Avg
Edge Breakdown
Our Model
59.6%
Books Say
55.6%
Edge
+7.2%
Im, Sungjae vs Rodgers, Patrick: Model gives Im, Sungjae 59.6% win probability vs 55.6% implied (+7.2% edge). Skill advantage: -0.26 SG/round. Expected finish: 33.
AI Intelligence Analysis
LEAN +0
Im 59.3% h2h vs 50.0% implied = +18.6% edge; Im's +1.13 course fit helps, but -0.26 SG skill gap vs Rodgers creates tension; edge is venue-dependent.
Key Factors
- Im course fit +1.13 SG (strong, second-best for Im)
- Skill diff -0.257 SG (Im is WEAKER overall player)
- BetOnline +100 (50% EV) vs 59.3% model = +18.6% edge
- Im EF 32.8 vs Rodgers (unknown top-40 profile)
Risk Factors
- Im's -0.257 SG skill gap is significant; edge entirely dependent on course fit validity
- Rodgers' profile unknown; could be comparable or stronger player
- Course fit +1.13 may not be sustained if venue/course conditions change
Edge Analysis
Moneyline
Im, Sungjae 59.6%
+7.2 pts
Spread
-0.3
+7.2 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. Full methodology →