Juan Manuel Cerundolo vs Alejandro Davidovich Fokina prediction for June 27, 2026: Our Monte Carlo simulation ran 10,000 game iterations and projects Alejandro Davidovich Fokina 0 - Juan Manuel Cerundolo 0. Alejandro Davidovich Fokina is favored with a 67.8% win probability.
Alejandro Davidovich Fokina
1652
Grass Elo
VS
Grass • ATP
Juan Manuel Cerundolo
1484
Grass Elo
Match Win Probability
Alejandro Davidovich FokinaJuan Manuel Cerundolo
Grass
Surface
ATP Wimbledon
Tournament
10,000
Simulations
Calibrated accuracy at this confidence: 65.2% (6,507 games)
Match Context
Tournament
ATP Wimbledon
Surface
Grass
Format
Best of 5 · ATP
Surface Elo Ratings (Grass)
Juan Manuel Cerundolo
Alejandro Davidovich Fokina
Alejandro Davidovich Fokina leads by 167 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%
Juan Manuel Cerundolo SPW
63.5%
Below tour avg
Alejandro Davidovich Fokina SPW
66.8%
Above tour avg
● Alejandro Davidovich Fokina has a significant serve advantage (+3.3%)
Market Odds & Model Edge
Juan Manuel Cerundolo ML
+329
Model: 32%
Edge: +8.9%
Alejandro Davidovich Fokina ML
-402
Model: 68%
Edge: -12.2%
Model Projection
Juan Manuel Cerundolo ML +329 · +8.9% edge
Key Matchup Factors
- Alejandro Davidovich Fokina holds a commanding 167-point Elo advantage on Grass
- Grass surface amplifies serve advantage — expect fewer breaks, more tiebreaks
- Alejandro Davidovich Fokina has the stronger serve profile on this surface
- Heavy favorite (Alejandro Davidovich Fokina at 68%) — ML value may be limited; consider live or set markets
Surface Elo v1.0 · Barnett-Clarke serve model · 10,000 simulations · ATP
Edge Analysis
Moneyline
Alejandro Davidovich Fokina 67.8%
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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 →