Johannus Monday / Harry Wendelken vs Corentin Moutet / Arthur Reymond prediction for July 1, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects Corentin Moutet / Arthur Reymond 0 - Johannus Monday / Harry Wendelken 0. Johannus Monday / Harry Wendelken is favored with a 53.2% win probability.
Corentin Moutet / Arthur Reymond
1500
Grass Elo
VS
Grass • ATP
Johannus Monday / Harry Wendelken
1500
Grass Elo
Match Win Probability
Corentin Moutet / Arthur ReymondJohannus Monday / Harry Wendelken
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)
Johannus Monday / Harry Wendelken
Corentin Moutet / Arthur Reymond
Johannus Monday / Harry Wendelken 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%
Johannus Monday / Harry Wendelken SPW
65.6%
Above tour avg
Corentin Moutet / Arthur Reymond SPW
65.6%
Above tour avg
● Serve statistics are nearly identical — expect a close match
Market Odds & Model Edge
Johannus Monday / Harry Wendelken ML
-118
Model: 53%
Edge: -0.9%
Corentin Moutet / Arthur Reymond ML
-103
Model: 47%
Edge: -4.0%
Key Matchup Factors
- Players are closely matched (0-point Elo gap)
- Grass surface amplifies serve advantage — expect fewer breaks, more tiebreaks
- Corentin Moutet / Arthur Reymond has the stronger serve profile on this surface
Surface Elo v1.0 · Barnett-Clarke serve model · 10,000 simulations · ATP
Edge Analysis
Moneyline
Johannus Monday / Harry Wendelken 53.2%
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More Projections Today
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 →