Suber, Jackson vs Coody, Pierceson prediction for July 21, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects Coody, Pierceson 92 - Suber, Jackson 76. Suber, Jackson is favored with a 55.9% win probability. The spread is -0.14.
Coody, Pierceson
+0.36
Strokes Gained / Round
VS
H2H • 3M Open
Suber, Jackson
+0.46
Strokes Gained / Round
Head-to-Head Win Probability
Coody, PiercesonSuber, Jackson
+105
Best Odds
Projected Points Range 10th – 90th percentile
Suber, Jackson
697683
Coody, Pierceson
859299
Tournament Context
Event
3M Open
Course
TPC Twin Cities
Field
144 players
Wind
12 mph
Temp
81°F
Conditions
harder (+0.6)
AI Intelligence Analysis
STRONG BET +1
Suber's +0.127 course fit and +0.455 SG total advantage significantly underbaked in market (55.7% model vs 48.8% implied); modest skill deficit (-0.135 SG) is outweighed by structural edge from course and form.
Key Factors
- Model edge: +14.2% probability (55.7% vs 48.8% market), largest pure edge in slate
- Course fit advantage: Suber +0.127 vs Coody -0.01 = +0.137 structural benefit at TPC Twin Cities
- SG total: Suber 0.455 vs Coody 0.59 is close, but Suber's consistency (low variance profile) suits course
- Odds +105 (caesars best): implies 48.78%, our model 55.71% = +700 bps edge with decent CLV potential
- Expected finish: Suber 76.3 (better than Coody's implied ranking) suggests tournament upside
Risk Factors
- Negative skill differential (-0.135 SG/round) could manifest if short-game execution matters; monitor early rounds
- Coody is not in field strength mismatch — both are mid-tier quality (78-79 expected finish historical support)
- Matchup brier (0.2486) suggests 3-4% residual variance; respect that uncertainty
Edge Analysis
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 →