Mac Kiger / Patrik Trhac vs Quentin Halys / Pierre-Hugues Herbert prediction for July 1, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects Quentin Halys / Pierre-Hugues Herbert 0 - Mac Kiger / Patrik Trhac 0. Quentin Halys / Pierre-Hugues Herbert is favored with a 51.1% win probability.
Quentin Halys / Pierre-Hugues Herbert
1500
Grass Elo
VS
Grass • ATP
Mac Kiger / Patrik Trhac
1500
Grass Elo
Match Win Probability
Quentin Halys / Pierre-Hugues HerbertMac Kiger / Patrik Trhac
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)
Mac Kiger / Patrik Trhac
Quentin Halys / Pierre-Hugues Herbert
Mac Kiger / Patrik Trhac 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%
Mac Kiger / Patrik Trhac SPW
65.6%
Above tour avg
Quentin Halys / Pierre-Hugues Herbert SPW
65.6%
Above tour avg
● Serve statistics are nearly identical — expect a close match
Market Odds & Model Edge
Mac Kiger / Patrik Trhac ML
+288
Model: 49%
Edge: +23.1%
Quentin Halys / Pierre-Hugues Herbert ML
-376
Model: 51%
Edge: -27.9%
Model Projection
Mac Kiger / Patrik Trhac ML +288 · +23.1% edge
Key Matchup Factors
- Players are closely matched (0-point Elo gap)
- Grass surface amplifies serve advantage — expect fewer breaks, more tiebreaks
- Quentin Halys / Pierre-Hugues Herbert has the stronger serve profile on this surface
Surface Elo v1.0 · Barnett-Clarke serve model · 10,000 simulations · ATP
Edge Analysis
Moneyline
Quentin Halys / Pierre-Hugues Herbert 51.1%
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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 →