Karen Khachanov vs Botic van de Zandschulp: AI Prediction | Games, Spread, Aces & Double Faults
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Set it upRanking Trends
Karen Khachanov
- Current #15 2026-05-04
- 3 mo ago #18 2026-02-02
- 6 mo ago #18 2025-11-03
- 9 mo ago #12 2025-08-04
- 12 mo ago #24 2025-05-05
Botic van de Zandschulp
- Current #54 2026-05-04
- 3 mo ago #67 2026-02-02
- 6 mo ago #80 2025-11-03
- 9 mo ago #92 2025-08-04
- 12 mo ago #85 2025-05-05
Player Metrics
Karen Khachanov
Botic van de Zandschulp
Recent Matches
Karen Khachanov
- Last Match: vs Aleksandr Shevchenko (2-0) clay Rome 103 min
- 2nd Last Match: vs Jakub Mensik (0-2) clay Madrid 107 min
- 3rd Last Match: vs Adam Walton (2-0) clay Madrid 79 min
- 4th Last Match: vs Camilo Ugo Carabelli (0-2) clay Barcelona 73 min
- 5th Last Match: vs Arthur Rinderknech (0-2) clay Monte Carlo 91 min
Botic van de Zandschulp
- Last Match: vs Aleksandar Kovacevic (2-1) clay Rome 127 min
- 2nd Last Match: vs Alexandre Muller (2-0) clay Rome 92 min
- 3rd Last Match: vs Francisco Cerundolo (0-2) clay Munich 64 min
- 4th Last Match: vs Marc-Andrea Huesler (2-0) clay Munich 110 min
- 5th Last Match: vs Mariano Navone (1-2) clay Bucharest 211 min
Head-to-Head (Last 2 Seasons)
Key Prediction Insights
Match Analysis
Total Games Predictions
Total Games Probability Distribution
Distribution
Probability of each total games outcome
Cumulative Probability (CDF)
Probability of total games ≤ X
Games Spread Predictions
Games Spread Probability Distribution
Distribution
Probability of each games spread outcome
Cumulative Probability (CDF)
Probability of spread ≤ X
Aces and Double Faults Predictions
Aces Probability Distribution
Distribution
Probability of each ace count outcome
Cumulative Probability (CDF)
Probability of aces ≤ X
Double Faults Probability Distribution
Distribution
Probability of each double fault count outcome
Cumulative Probability (CDF)
Probability of double faults ≤ X
Match Format Predictions
Tiebreak Likelihood
Probability that any tiebreak is played in this match
Exact Score Distribution BO3
Probability of each set-by-set outcome (Karen Khachanov's perspective)
Full distributions as structured data (JSON or CSV)
Final Prediction
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