Aberg, Ludvig vs Fitzpatrick, Matt prediction for May 5, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects Fitzpatrick, Matt 34 - Aberg, Ludvig 23. Aberg, Ludvig is favored with a 59.8% win probability. The spread is -0.29.
Fitzpatrick, Matt
+2.00
Strokes Gained / Round
VS
H2H • Truist Championship
Aberg, Ludvig
+1.71
Strokes Gained / Round
Head-to-Head Win Probability
Fitzpatrick, MattAberg, Ludvig
+101
Best Odds
+20.3%
Edge
1.5u HIGH
Sizing
Projected Points Range 10th – 90th percentile
Aberg, Ludvig
162330
Fitzpatrick, Matt
273441
Tournament Context
Event
Truist Championship
Course
Quail Hollow Club
Field
72 players
Wind
11 mph
Temp
76°F
Conditions
harder (+0.5)
Player Profile — Aberg, Ludvig
Strokes Gained
+1.71/round
World Class
Course Fit
neutral
+0.145 SG adj
Expected Finish
23th / 72
Matchup Analysis
Aberg, Ludvig
+1.71 SG
EF 23th
Skill Gap
-0.29 SG/round
tight edge for Fitzpatrick, Matt
Fitzpatrick, Matt
+2.00 SG
EF 34th · World Class
Edge Breakdown
Our Model
59.8%
Books Say
49.8%
Edge
+20.3%
Aberg, Ludvig vs Fitzpatrick, Matt: Model gives Aberg, Ludvig 59.8% win probability vs 49.8% implied (+20.3% edge). Skill advantage: -0.29 SG/round. Expected finish: 23.
AI Intelligence Analysis
LEAN +0
Aberg 59.9% h2h vs 50.0% implied = +19.8% edge; but Aberg's +1.71 SG total + -0.29 SG skill gap vs Fitzpatrick (unknown top-40 profile) + weak +0.147 course fit creates model tension.
Key Factors
- Aberg SG +1.71 (solid mid-tier skill)
- Course fit +0.147 SG (minimal venue advantage)
- Skill diff -0.294 SG (Aberg is WEAKER than Fitzpatrick)
- Pinnacle +100 (50% EV) vs 59.9% model = +19.8% edge
Risk Factors
- Aberg has -0.294 SG skill deficit; edge is pure skill variance, not venue-based
- Fitzpatrick's profile unknown; could be significantly stronger player
- Course fit +0.147 is negligible; Aberg is betting on skill outlier in h2h
Edge Analysis
Moneyline
Aberg, Ludvig 59.8%
+20.3 pts
Spread
-0.3
+20.3 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 →