Raphael Collignon / Dino Prizmic vs Roman Andres Burruchaga / Thiago Agustin Tirante prediction for July 1, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects Roman Andres Burruchaga / Thiago Agustin Tirante 0 - Raphael Collignon / Dino Prizmic 0. Roman Andres Burruchaga / Thiago Agustin Tirante is favored with a 53.5% win probability.
Roman Andres Burruchaga / Thiago Agustin Tirante
1500
Grass Elo
VS
Grass • ATP
Raphael Collignon / Dino Prizmic
1500
Grass Elo
Match Win Probability
Roman Andres Burruchaga / Thiago Agustin TiranteRaphael Collignon / Dino Prizmic
Grass
Surface
ATP Wimbledon Doubles
Tournament
10,000
Simulations
Calibrated accuracy at this confidence: 54.2% (6,507 games)
Match Context
Tournament
ATP Wimbledon Doubles
Surface
Grass
Format
Best of 5 · ATP
Surface Elo Ratings (Grass)
Raphael Collignon / Dino Prizmic
Roman Andres Burruchaga / Thiago Agustin Tirante
Raphael Collignon / Dino Prizmic 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%
Raphael Collignon / Dino Prizmic SPW
65.6%
Above tour avg
Roman Andres Burruchaga / Thiago Agustin Tirante SPW
65.6%
Above tour avg
● Serve statistics are nearly identical — expect a close match
Market Odds & Model Edge
Raphael Collignon / Dino Prizmic ML
+125
Model: 46%
Edge: +2.1%
Roman Andres Burruchaga / Thiago Agustin Tirante ML
-155
Model: 54%
Edge: -7.3%
Key Matchup Factors
- Players are closely matched (0-point Elo gap)
- Grass surface amplifies serve advantage — expect fewer breaks, more tiebreaks
- Roman Andres Burruchaga / Thiago Agustin Tirante has the stronger serve profile on this surface
Surface Elo v1.0 · Barnett-Clarke serve model · 10,000 simulations · ATP
Edge Analysis
Moneyline
Roman Andres Burruchaga / Thiago Agustin Tirante 53.5%
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More Projections Today
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 →