Doha Qatar Hard Atp 500 Round of 32

Jesper de Jong vs Andrey Rublev: AI Prediction | Games, Spread, Aces & Double Faults

Jesper de Jong

Rank: #86
24%
VS

Andrey Rublev

Rank: #15
76%
Expected Total Games: 23.9
Predicted Winner: Andrey Rublev

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Player Metrics

Jesper de Jong

Form Index: 10.2
ELO Rating: 737.9
Glicko2 Rating: 1546.2
Current Fatigue (minutes): 0.0
Surface Strength:
Hard: 5.5
Clay: 7.3
Grass: 5.7
Serve Rating: 71.2
Return Rating: 54.0

Andrey Rublev

Form Index: 60.5
ELO Rating: 2400.8
Glicko2 Rating: 1847.4
Current Fatigue (minutes): 0.0
Surface Strength:
Hard: 14.5
Clay: 20.3
Grass: 20.7
Serve Rating: 97.8
Return Rating: 90.4

Recent Matches

Jesper de Jong

Andrey Rublev

Head-to-Head (Last 2 Seasons)

0
Jesper de Jong
vs
1
Andrey Rublev
Hard
0 - 0
Clay
0 - 1
Grass
0 - 0

Key Prediction Insights

At the 500-level event in Doha, Qatar, Jesper de Jong faces Andrey Rublev in the round of 32 on hard court. The model predicts Andrey Rublev to win (76.10%) while Jesper de Jong has a 23.90% chance, with an expected total of about 23.85 games in the match.

Match Analysis

De Jong (rank 86) arrives with a low form index (10.22) and an Elo of 737.89; his surface strength index is modest (5.52) and fatigue is minimal. Rublev (rank 15) shows markedly stronger metrics: a form index of 60.48 and a much higher Elo (2400.82), with a surface strength index of 14.52 and no accumulated fatigue. The difference in mean serve index is large (Rublev 97.77 vs de Jong 71.18), and the mean return index gap is also substantial (Rublev 90.41 vs de Jong 53.99), both exceeding the 5-point threshold—this clearly favors Rublev on both serve and return dynamics. Looking at recent results, de Jong has three straight losses in his last three hard-court matches (Adelaide, Australian Open, Rotterdam), managing only a set in two of those matches and enduring long match durations. Rublev’s recent form is stronger overall: two wins followed by a loss at the Australian Open; his recent matches include straight-set and four-set victories before a tougher result, indicating continued high-level performance despite a recent defeat.

Total Games Predictions

🎾
Expected Total Games in Match 23.9 Most likely outcome: 23 games

📊 Total Games Probability Distribution

Distribution

Probability of each total games outcome

Probability distribution chart for total games in Jesper de Jong versus Andrey Rublev. 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 Jesper de Jong versus Andrey Rublev. The curve rises from 0% to 100%, showing the cumulative probability for each games total threshold.

Games Spread Predictions

📈
Expected Games Spread (Jesper de Jong - Andrey Rublev) +0.4 Most likely spread: 0 (even number of games won)

📊 Games Spread Probability Distribution

Distribution

Probability of each games spread outcome

Probability distribution chart for games spread in Jesper de Jong versus Andrey Rublev. Positive values indicate Jesper de Jong winning more games, negative values indicate Andrey Rublev winning more games.
Cumulative Probability (CDF)

Probability of spread ≤ X

Cumulative distribution function chart for games spread in Jesper de Jong versus Andrey Rublev. The curve shows the cumulative probability for each spread threshold.

Aces and Double Faults Predictions

The aces prediction for the match is 16.46 total aces and an expected double faults figure of 5.44. On Doha’s medium-paced hard court, the predicted aces reflect a balanced surface where big serving is rewarded but not as much as on faster courts—this aligns with the predicted aces. Given Rublev’s substantially higher serve rating, the predicted aces distribution will likely skew toward him, and the double faults prediction suggests both players will keep errors moderate.
🎯
Expected Total Aces 16.5 Most likely: 16 aces
Expected Total Double Faults 5.4 Most likely: 5 double faults

🎯 Aces Probability Distribution

Distribution

Probability of each ace count outcome

Probability distribution chart for total aces in Jesper de Jong versus Andrey Rublev. 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 Jesper de Jong versus Andrey Rublev. 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 Jesper de Jong versus Andrey Rublev. 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 Jesper de Jong versus Andrey Rublev. The curve shows the cumulative probability for each double faults threshold.

Final Prediction

Rublev’s superior rank, Elo, form and large advantages in serve and return indices give him the clear edge. The key factor to watch is Rublev’s serve and how effectively de Jong can neutralize it with returns early in rallies.

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