Rose, Justin vs Thomas, Justin prediction for May 5, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects Thomas, Justin 67 - Rose, Justin 38. Rose, Justin is favored with a 58.4% win probability. The spread is 0.1.
Thomas, Justin
+0.87
Strokes Gained / Round
VS
H2H • PGA Championship
Rose, Justin
+0.97
Strokes Gained / Round
Head-to-Head Win Probability
Thomas, JustinRose, Justin
+113
Best Odds
+24.5%
Edge
1.5u ELITE
Sizing
Projected Points Range 10th – 90th percentile
Rose, Justin
313845
Thomas, Justin
606774
Tournament Context
Event
PGA Championship
Course
Aronimink Golf Club
Field
156 players
Player Profile — Rose, Justin
Strokes Gained
+0.97/round
Above Avg
Course Fit
excellent
+1.998 SG adj
Expected Finish
38th / 156
Matchup Analysis
Rose, Justin
+0.97 SG
EF 38th
Skill Gap
+0.10 SG/round
tight edge for Rose, Justin
Thomas, Justin
+0.87 SG
EF 67th · Above Avg
Edge Breakdown
Our Model
58.4%
Books Say
46.9%
Edge
+24.5%
Rose, Justin vs Thomas, Justin: Model gives Rose, Justin 58.4% win probability vs 46.9% implied (+24.5% edge). Skill advantage: +0.10 SG/round. Expected finish: 38.
AI Intelligence Analysis
NEUTRAL +0
Thomas 56.0% h2h vs 52.6% implied = +6.5% edge; Thomas' solid +0.77 SG total + negative -0.181 course fit + negative -0.294 SG skill gap = marginal edge at venue disadvantage.
Key Factors
- Thomas SG +0.769 (solid mid-tier, EF 40.2)
- Course fit -0.181 SG (negative venue disadvantage)
- Skill diff -0.294 SG (Thomas WEAKER than Rose)
- FanDuel -111 (52.6% implied) vs 56.0% model = +6.5% edge
Risk Factors
- Thomas has negative course fit (-0.181 SG) + skill disadvantage (-0.294 SG)
- Tiny edge (6.5%) with structural headwinds
- Rose is likely better player at this venue
Edge Analysis
Moneyline
Rose, Justin 58.4%
+24.5 pts
Spread
+0.1
+24.5 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 →