Vasil Kirkov / Bart Stevens vs Sadio Doumbia / Fabien Reboul prediction for July 1, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects Sadio Doumbia / Fabien Reboul 0 - Vasil Kirkov / Bart Stevens 0. Vasil Kirkov / Bart Stevens is favored with a 51.4% win probability.
Sadio Doumbia / Fabien Reboul
1500
Grass Elo
VS
Grass • ATP
Vasil Kirkov / Bart Stevens
1500
Grass Elo
Match Win Probability
Sadio Doumbia / Fabien ReboulVasil Kirkov / Bart Stevens
Grass
Surface
ATP Wimbledon Doubles
Tournament
10,000
Simulations
Calibrated accuracy at this confidence: 54.0% (6,507 games)
Match Context
Tournament
ATP Wimbledon Doubles
Surface
Grass
Format
Best of 5 · ATP
Surface Elo Ratings (Grass)
Vasil Kirkov / Bart Stevens
Sadio Doumbia / Fabien Reboul
Vasil Kirkov / Bart Stevens leads by 0 Elo points on Grass
Serve & Return Analysis
Serve Points Won % (SPW) is the single most predictive metric in tennis. ATP average on Grass: 63.5%
Vasil Kirkov / Bart Stevens SPW
65.6%
Above tour avg
Sadio Doumbia / Fabien Reboul SPW
65.6%
Above tour avg
● Serve statistics are nearly identical — expect a close match
Market Odds & Model Edge
Vasil Kirkov / Bart Stevens ML
+142
Model: 51%
Edge: +10.1%
Sadio Doumbia / Fabien Reboul ML
-177
Model: 49%
Edge: -15.3%
Model Projection
Vasil Kirkov / Bart Stevens ML +142 · +10.1% edge
Key Matchup Factors
- Players are closely matched (0-point Elo gap)
- Grass surface amplifies serve advantage — expect fewer breaks, more tiebreaks
- Sadio Doumbia / Fabien Reboul has the stronger serve profile on this surface
Surface Elo v1.0 · Barnett-Clarke serve model · 10,000 simulations · ATP
Edge Analysis
Moneyline
Vasil Kirkov / Bart Stevens 51.4%
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How this prediction was generated: This page shows output from the Olympus Bets ATP/WTA Tennis 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 →