Monte Carlo Monaco Clay Masters 1000 Round of 64

Roberto Bautista Agut vs Matteo Berrettini: AI Prediction | Games, Spread, Aces & Double Faults

Roberto Bautista Agut

Rank: #80
24%
VS

Matteo Berrettini

Rank: #91
76%
Expected Total Games: 23.1
Predicted Winner: Matteo Berrettini

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Player Metrics

Roberto Bautista Agut

Form Index: 37.7
ELO Rating: 1538.7
Glicko2 Rating: 1534.5
Current Fatigue (minutes): 239.0
Surface Strength:
Hard: 51.4
Clay: 39.5
Grass: 41.1
Serve Rating: 75.1
Return Rating: 70.3

Matteo Berrettini

Form Index: 41.9
ELO Rating: 1632.3
Glicko2 Rating: 1658.2
Current Fatigue (minutes): 0.0
Surface Strength:
Hard: 58.1
Clay: 40.9
Grass: 40.2
Serve Rating: 99.6
Return Rating: 88.2

Recent Matches

Roberto Bautista Agut

  • Last Match: vs Alexander Shevchenko (0-2) clay Monte Carlo 132 min
  • 2nd Last Match: vs Benjamin Bonzi (2-0) clay Monte Carlo 107 min
  • 3rd Last Match: vs Titouan Droguet (1-2) clay Bucharest 100 min
  • 4th Last Match: vs Karen Khachanov (0-2) hard Miami 73 min
  • 5th Last Match: vs James Duckworth (2-0) hard Miami 126 min

Matteo Berrettini

  • Last Match: vs Ignacio Buse (1-2) clay Marrakech 181 min
  • 2nd Last Match: vs Valentin Vacherot (0-2) hard Miami 91 min
  • 3rd Last Match: vs Alexander Bublik (2-0) hard Miami 88 min
  • 4th Last Match: vs Alexandre Muller (2-0) hard Miami 82 min
  • 5th Last Match: vs Alexander Zverev (0-2) hard Indian Wells 71 min

Head-to-Head (Last 2 Seasons)

0
Roberto Bautista Agut
vs
0
Matteo Berrettini
Hard
0 - 0
Clay
0 - 0
Grass
0 - 0

Key Prediction Insights

Monte Carlo, Round of 64 on red clay at a Masters 1000 event sets up Roberto Bautista Agut versus Matteo Berrettini. The model favors Berrettini, with a 75.64% chance to win versus 24.36% for Bautista Agut, and an expected match length of about 23.1 games.

Match Analysis

Bautista Agut (rank 80) brings a solid baseline game and an Elo of 1538.7, but his form index (37.66) and surface strength (39.46) suggest he is below his peak on clay right now. He also arrives with 239 minutes of cumulative court time at this event, which shows notable fatigue. Berrettini (rank 91) posts a higher Elo (1632.3) and a slightly better form index (41.94) and surface strength (40.93), while listing zero tournament fatigue — a freshness edge in Monte Carlo’s physical conditions. Serve and return metrics are decisive here: Berrettini’s mean serve index (99.57) is dramatically higher than Bautista Agut’s (75.08), and his mean return index (88.24) also outstrips Bautista Agut’s (70.30). Both differences exceed 5 points and point to a clear advantage in both serving potency and return quality for Berrettini. Recent match patterns underline some inconsistency: Bautista Agut has one win (over Bonzi) and two defeats in his last three matches, including a straight-sets loss at this event; Berrettini has one win and two losses in his past three, with his most recent matches indicating longer, more physical contests.

Total Games Predictions

🎾
Expected Total Games in Match 23.1 Most likely outcome: 23 games

📊 Total Games Probability Distribution

Distribution

Probability of each total games outcome

Probability distribution chart for total games in Roberto Bautista Agut versus Matteo Berrettini. The X-axis shows possible total games values and the Y-axis shows the predicted probability percentage for each outcome.
Cumulative Probability (CDF)

Probability of total games ≤ X

Cumulative distribution function chart for total games in Roberto Bautista Agut versus Matteo Berrettini. The curve rises from 0% to 100%, showing the cumulative probability for each games total threshold.

Games Spread Predictions

📈
Expected Games Spread (Roberto Bautista Agut - Matteo Berrettini) +2.5 Most likely spread: +2 (Roberto Bautista Agut wins 2 more games)

📊 Games Spread Probability Distribution

Distribution

Probability of each games spread outcome

Probability distribution chart for games spread in Roberto Bautista Agut versus Matteo Berrettini. Positive values indicate Roberto Bautista Agut winning more games, negative values indicate Matteo Berrettini winning more games.
Cumulative Probability (CDF)

Probability of spread ≤ X

Cumulative distribution function chart for games spread in Roberto Bautista Agut versus Matteo Berrettini. The curve shows the cumulative probability for each spread threshold.

Aces and Double Faults Predictions

The aces prediction for the match is 15.68 total; the predicted aces lean toward Berrettini given his far superior serve index. The double faults prediction sits at 5.05 expected double faults for the match. On clay, the slower surface typically reduces aces and can increase double faults through longer rallies and physical wear, so these figures reflect both the surface and Berrettini’s serving dominance.
🎯
Expected Total Aces 15.7 Most likely: 15 aces
Expected Total Double Faults 5.0 Most likely: 5 double faults

🎯 Aces Probability Distribution

Distribution

Probability of each ace count outcome

Probability distribution chart for total aces in Roberto Bautista Agut versus Matteo Berrettini. Higher ace counts are more likely on faster surfaces like grass.
Cumulative Probability (CDF)

Probability of aces ≤ X

Cumulative distribution function chart for total aces in Roberto Bautista Agut versus Matteo Berrettini. The curve shows the cumulative probability for each aces threshold.

Double Faults Probability Distribution

Distribution

Probability of each double fault count outcome

Probability distribution chart for double faults in Roberto Bautista Agut versus Matteo Berrettini. Clay surface matches tend to produce more double faults due to fatigue in longer rallies.
Cumulative Probability (CDF)

Probability of double faults ≤ X

Cumulative distribution function chart for double faults in Roberto Bautista Agut versus Matteo Berrettini. The curve shows the cumulative probability for each double faults threshold.

Final Prediction

Berrettini’s higher Elo, overpowering serve metrics and lack of tournament fatigue create the edge in this matchup. Key factor to watch: whether Bautista Agut’s return consistency can neutralize Berrettini’s serve early, especially in short, decisive service games.

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