Orlando Luz / Rafael Matos vs Theo Arribage / Albano Olivetti prediction for July 1, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects Theo Arribage / Albano Olivetti 0 - Orlando Luz / Rafael Matos 0. Theo Arribage / Albano Olivetti is favored with a 50.2% win probability.
Theo Arribage / Albano Olivetti
1500
Grass Elo
VS
Grass • ATP
Orlando Luz / Rafael Matos
1500
Grass Elo
Match Win Probability
Theo Arribage / Albano OlivettiOrlando Luz / Rafael Matos
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)
Orlando Luz / Rafael Matos
Theo Arribage / Albano Olivetti
Orlando Luz / Rafael Matos 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%
Orlando Luz / Rafael Matos SPW
65.6%
Above tour avg
Theo Arribage / Albano Olivetti SPW
65.6%
Above tour avg
● Serve statistics are nearly identical — expect a close match
Market Odds & Model Edge
Orlando Luz / Rafael Matos ML
+220
Model: 50%
Edge: +18.6%
Theo Arribage / Albano Olivetti ML
-284
Model: 50%
Edge: -23.8%
Model Projection
Orlando Luz / Rafael Matos ML +220 · +18.6% edge
Key Matchup Factors
- Players are closely matched (0-point Elo gap)
- Grass surface amplifies serve advantage — expect fewer breaks, more tiebreaks
- Theo Arribage / Albano Olivetti has the stronger serve profile on this surface
Surface Elo v1.0 · Barnett-Clarke serve model · 10,000 simulations · ATP
Edge Analysis
Moneyline
Theo Arribage / Albano Olivetti 50.2%
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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. Probabilities are calibrated using Bayesian methods and sized via the Kelly Criterion. Full methodology →