Austin Krajicek / Nikola Mektic vs Marcelo Melo / Andres Molteni prediction for July 1, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects Marcelo Melo / Andres Molteni 0 - Austin Krajicek / Nikola Mektic 0. Austin Krajicek / Nikola Mektic is favored with a 50.0% win probability.
Marcelo Melo / Andres Molteni
1500
Grass Elo
VS
Grass • ATP
Austin Krajicek / Nikola Mektic
1500
Grass Elo
Match Win Probability
Marcelo Melo / Andres MolteniAustin Krajicek / Nikola Mektic
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)
Austin Krajicek / Nikola Mektic
Marcelo Melo / Andres Molteni
Austin Krajicek / Nikola Mektic 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%
Austin Krajicek / Nikola Mektic SPW
65.6%
Above tour avg
Marcelo Melo / Andres Molteni SPW
65.6%
Above tour avg
● Serve statistics are nearly identical — expect a close match
Market Odds & Model Edge
Austin Krajicek / Nikola Mektic ML
-212
Model: 50%
Edge: -18.0%
Marcelo Melo / Andres Molteni ML
+172
Model: 50%
Edge: +13.3%
Model Projection
Marcelo Melo / Andres Molteni ML +172 · +13.3% edge
Key Matchup Factors
- Players are closely matched (0-point Elo gap)
- Grass surface amplifies serve advantage — expect fewer breaks, more tiebreaks
- Marcelo Melo / 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
Austin Krajicek / Nikola Mektic 50.0%
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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 →