Guido Andreozzi / Manuel Guinard vs Pablo Carreno Busta / Jaume Munar prediction for July 1, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects Pablo Carreno Busta / Jaume Munar 0 - Guido Andreozzi / Manuel Guinard 0. Guido Andreozzi / Manuel Guinard is favored with a 51.6% win probability.
Pablo Carreno Busta / Jaume Munar
1500
Grass Elo
VS
Grass • ATP
Guido Andreozzi / Manuel Guinard
1500
Grass Elo
Match Win Probability
Pablo Carreno Busta / Jaume MunarGuido Andreozzi / Manuel Guinard
Grass
Surface
ATP Wimbledon Doubles
Tournament
10,000
Simulations
Calibrated accuracy at this confidence: 54.1% (6,507 games)
Match Context
Tournament
ATP Wimbledon Doubles
Surface
Grass
Format
Best of 5 · ATP
Surface Elo Ratings (Grass)
Guido Andreozzi / Manuel Guinard
Pablo Carreno Busta / Jaume Munar
Guido Andreozzi / Manuel Guinard 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%
Guido Andreozzi / Manuel Guinard SPW
65.5%
Above tour avg
Pablo Carreno Busta / Jaume Munar SPW
65.6%
Above tour avg
● Serve statistics are nearly identical — expect a close match
Market Odds & Model Edge
Guido Andreozzi / Manuel Guinard ML
-398
Model: 52%
Edge: -28.3%
Pablo Carreno Busta / Jaume Munar ML
+295
Model: 48%
Edge: +23.1%
Model Projection
Pablo Carreno Busta / Jaume Munar ML +295 · +23.1% edge
Key Matchup Factors
- Players are closely matched (0-point Elo gap)
- Grass surface amplifies serve advantage — expect fewer breaks, more tiebreaks
- Pablo Carreno Busta / Jaume Munar has the stronger serve profile on this surface
Surface Elo v1.0 · Barnett-Clarke serve model · 10,000 simulations · ATP
Edge Analysis
Moneyline
Guido Andreozzi / Manuel Guinard 51.6%
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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 →