Alexander Bublik / Nick Kyrgios vs Marcelo Arevalo / Mate Pavic prediction for July 1, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects Marcelo Arevalo / Mate Pavic 0 - Alexander Bublik / Nick Kyrgios 0. Marcelo Arevalo / Mate Pavic is favored with a 51.7% win probability.
Marcelo Arevalo / Mate Pavic
1500
Grass Elo
VS
Grass • ATP
Alexander Bublik / Nick Kyrgios
1500
Grass Elo
Match Win Probability
Marcelo Arevalo / Mate PavicAlexander Bublik / Nick Kyrgios
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)
Alexander Bublik / Nick Kyrgios
Marcelo Arevalo / Mate Pavic
Alexander Bublik / Nick Kyrgios 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%
Alexander Bublik / Nick Kyrgios SPW
65.6%
Above tour avg
Marcelo Arevalo / Mate Pavic SPW
65.6%
Above tour avg
● Serve statistics are nearly identical — expect a close match
Market Odds & Model Edge
Alexander Bublik / Nick Kyrgios ML
+363
Model: 48%
Edge: +26.7%
Marcelo Arevalo / Mate Pavic ML
-493
Model: 52%
Edge: -31.5%
Model Projection
Alexander Bublik / Nick Kyrgios ML +363 · +26.7% edge
Key Matchup Factors
- Players are closely matched (0-point Elo gap)
- Grass surface amplifies serve advantage — expect fewer breaks, more tiebreaks
- Marcelo Arevalo / Mate Pavic has the stronger serve profile on this surface
Surface Elo v1.0 · Barnett-Clarke serve model · 10,000 simulations · ATP
Edge Analysis
Moneyline
Marcelo Arevalo / Mate Pavic 51.7%
--
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 →