Francisco Cabral / Joe Salisbury vs Maximo Gonzalez / Andres Molteni prediction for April 30, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects Maximo Gonzalez / Andres Molteni 0 - Francisco Cabral / Joe Salisbury 0. Maximo Gonzalez / Andres Molteni is favored with a 54.2% win probability.
Maximo Gonzalez / Andres Molteni
1500
Clay Elo
VS
Clay • ATP
Francisco Cabral / Joe Salisbury
1500
Clay Elo
Match Win Probability
Maximo Gonzalez / Andres MolteniFrancisco Cabral / Joe Salisbury
Clay
Surface
ATP Madrid - Doubles
Tournament
10,000
Simulations
Calibrated accuracy at this confidence: 58.4% (1,363 games)
Match Context
Tournament
ATP Madrid - Doubles
Surface
Clay
Format
Best of 3 · ATP
Surface Elo Ratings (Clay)
Francisco Cabral / Joe Salisbury
Maximo Gonzalez / Andres Molteni
Francisco Cabral / Joe Salisbury leads by 0 Elo points on Clay
Serve & Return Analysis
Serve Points Won % (SPW) is the single most predictive metric in tennis. ATP average on Clay: 63.5%
Francisco Cabral / Joe Salisbury SPW
60.3%
Below tour avg
Maximo Gonzalez / Andres Molteni SPW
60.3%
Below tour avg
● Serve statistics are nearly identical — expect a close match
Market Odds & Model Edge
Francisco Cabral / Joe Salisbury ML
-161
Model: 46%
Edge: -15.9%
Maximo Gonzalez / Andres Molteni ML
+132
Model: 54%
Edge: +11.1%
Model Projection
Maximo Gonzalez / Andres Molteni ML +132 · +11.1% edge
Key Matchup Factors
- Players are closely matched (0-point Elo gap)
- Clay surface reduces serve dominance — expect more breaks of serve and longer rallies
- Maximo Gonzalez / Andres Molteni has the stronger serve profile on this surface
Surface Elo v1.0 · Barnett-Clarke serve model · 10,000 simulations · ATP
Edge Analysis
Moneyline
Maximo Gonzalez / Andres Molteni 54.2%
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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. Full methodology →