Brennan, Michael vs Kim, Tom prediction for May 21, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects Kim, Tom 70 - Brennan, Michael 81. Brennan, Michael is favored with a 58.6% win probability. The spread is -0.02.
Kim, Tom
+0.21
Strokes Gained / Round
VS
H2H • THE CJ CUP Byron Nelson
Brennan, Michael
+0.22
Strokes Gained / Round
Head-to-Head Win Probability
Kim, TomBrennan, Michael
+111
Best Odds
+23.6%
Edge
1.5u HIGH
Sizing
Projected Points Range 10th – 90th percentile
Brennan, Michael
748188
Kim, Tom
637077
Tournament Context
Event
THE CJ CUP Byron Nelson
Course
TPC Craig Ranch
Field
147 players
Wind
15 mph
Temp
82°F
Conditions
harder (+0.8)
Player Profile — Brennan, Michael
Strokes Gained
+0.22/round
Tour Avg
Course Fit
good
+0.281 SG adj
Expected Finish
81th / 147
Matchup Analysis
Brennan, Michael
+0.22 SG
EF 81th
Skill Gap
-0.02 SG/round
essentially a coin flip
Kim, Tom
+0.21 SG
EF 70th · Tour Avg
Edge Breakdown
Our Model
58.6%
Books Say
47.4%
Edge
+23.6%
Brennan, Michael vs Kim, Tom: Model gives Brennan, Michael 58.6% win probability vs 47.4% implied (+23.6% edge). Expected finish: 81.
AI Intelligence Analysis
STRONG BET +1GREEN ZONE0.6% WR (n=380)
Brennan's +0.216 SG total and +0.281 course fit create +17.0% edge despite near-zero skill gap (-0.022); venue specialization drives edge at 58.48% vs 50.0% market.
Key Factors
- Course fit: +0.281 (Brennan advantage at TPC Craig Ranch)
- SG total: +0.216 (Brennan overall better)
- EF gap: 81.0 vs Kim higher, shows Brennan better positioned
- Edge: +17.0% with even odds (+100) — solid value
- No skill gap issue: -0.022 means neither player dominates
Risk Factors
- Kim is top-50 PGA player; not weak opposition
- Mid-field matchup (~81 EF); moderate variance
- Even odds suggest sharp books value both equally despite model edge
COURSE FITLINE VALUEMID FIELD EDGE
Edge Analysis
Moneyline
Brennan, Michael 58.6%
+23.6 pts
Spread
-0.0
+23.6 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 →