Marton Fucsovics / Fabian Marozsan vs Ignacio Buse / Marco Trungelliti prediction for July 1, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects Ignacio Buse / Marco Trungelliti 0 - Marton Fucsovics / Fabian Marozsan 0. Marton Fucsovics / Fabian Marozsan is favored with a 56.5% win probability.
Ignacio Buse / Marco Trungelliti
1500
Grass Elo
VS
Grass • ATP
Marton Fucsovics / Fabian Marozsan
1500
Grass Elo
Match Win Probability
Ignacio Buse / Marco TrungellitiMarton Fucsovics / Fabian Marozsan
Grass
Surface
ATP Wimbledon Doubles
Tournament
10,000
Simulations
Calibrated accuracy at this confidence: 54.3% (6,507 games)
Match Context
Tournament
ATP Wimbledon Doubles
Surface
Grass
Format
Best of 5 · ATP
Surface Elo Ratings (Grass)
Marton Fucsovics / Fabian Marozsan
Ignacio Buse / Marco Trungelliti
Marton Fucsovics / Fabian Marozsan 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%
Marton Fucsovics / Fabian Marozsan SPW
66.5%
Above tour avg
Ignacio Buse / Marco Trungelliti SPW
65.8%
Above tour avg
● Serve statistics are nearly identical — expect a close match
Market Odds & Model Edge
Marton Fucsovics / Fabian Marozsan ML
-189
Model: 56%
Edge: -8.9%
Ignacio Buse / Marco Trungelliti ML
+151
Model: 44%
Edge: +3.7%
Key Matchup Factors
- Players are closely matched (0-point Elo gap)
- Grass surface amplifies serve advantage — expect fewer breaks, more tiebreaks
- Marton Fucsovics / Fabian Marozsan has the stronger serve profile on this surface
Surface Elo v1.0 · Barnett-Clarke serve model · 10,000 simulations · ATP
Edge Analysis
Moneyline
Marton Fucsovics / Fabian Marozsan 56.5%
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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 →