Us Open NY, U.S.A. Hard Grand Slam Round of 32

Alexander Blockx vs Flavio Cobolli: AI Prediction | Games, Spread, Aces & Double Faults

Alexander Blockx

Rank: #32
45%
VS

Flavio Cobolli

Rank: #6
55%
Expected Total Games: 35.0
Predicted Winner: Flavio Cobolli

Why the Model Favors Flavio Cobolli

The factors that drove this prediction, measured in win-probability points.

Overall strength +4.7 Flavio Cobolli
Recent form +3.3 Alexander Blockx
Recent record by level +2.2 Flavio Cobolli
Serve & return game +2.0 Alexander Blockx
Age +1.9 Alexander Blockx

Starting from an even matchup, these factors move the model to 55% for Flavio Cobolli. Computed with gradient-based attribution on our neural network, not editorial opinion. How to read this →

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Player Metrics

Alexander Blockx

Form Index: 63.7
ELO Rating: 1821.1
Glicko2 Rating: 1822.6
Current Fatigue (minutes): 348.0
Surface Strength:
Hard: 21.3
Clay: 32.7
Grass: 5.8
Serve Rating: 96.6
Return Rating: 88.8

Flavio Cobolli

Form Index: 74.6
ELO Rating: 1926.8
Glicko2 Rating: 1868.9
Current Fatigue (minutes): 348.0
Surface Strength:
Hard: 35.8
Clay: 37.7
Grass: 33.1
Serve Rating: 94.7
Return Rating: 86.6

Recent Matches

Alexander Blockx

Flavio Cobolli

Head-to-Head (Last 2 Seasons)

1
Alexander Blockx
vs
1
Flavio Cobolli
Hard
0 - 1
Clay
1 - 0
Grass
0 - 0

Key Prediction Insights

At the 2026 US Open in New York, Round of 32 on hard courts, the model gives Flavio Cobolli the edge over Alexander Blockx. Predicted winner: Flavio Cobolli (54.93%). Alexander Blockx is assigned a 45.07% chance. The model forecasts about 34.95 total games in the match.

Match Analysis

The model’s edge for Cobolli comes mainly from overall strength and recent record at this level. Cobolli brings the higher ranking and ratings to the contest, with a rank of 6 versus Blockx at 32, and an Elo of 1926.82 compared with Blockx at 1821.14. That overall strength factor accounts for the largest swing toward Cobolli, and the recent record by level also favors him, reflecting his results in comparable tournaments. At the same time the model gives credits toward Blockx from recent form, serve and return game, and age. Blockx’s form index is solid at 63.74 and his mean serve index is marginally higher at 96.59 versus Cobolli’s 94.70, while his mean return index is 88.81 versus Cobolli’s 86.59. Neither serve nor return gaps exceed 5 points, so they are supportive but not decisive. Both players arrive with identical cumulative fatigue in the event, 348 minutes on court. Surface strength favors Cobolli more, with a surface strength index of 35.76 against Blockx at 21.27, which aligns with the model’s tilt. Recent results show consistency for both at Flushing Meadows. Blockx won his two US Open matches after a three-set loss to Cobolli in Cincinnati earlier this season. Cobolli also won his two opening rounds at the US Open, and his earlier loss in Cincinnati was a straight sets defeat to Arthur Fils. Those patterns explain the mixed influence of recent form and level on the projection.

Total Games Predictions

🎾
Expected Total Games in Match 35.0 Most likely outcome: 33 games

📊 Total Games Probability Distribution

Distribution

Probability of each total games outcome

Probability distribution chart for total games in Alexander Blockx versus Flavio Cobolli. 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 Alexander Blockx versus Flavio Cobolli. The curve rises from 0% to 100%, showing the cumulative probability for each games total threshold.

Games Spread Predictions

📈
Expected Games Spread (Alexander Blockx - Flavio Cobolli) -0.8 Most likely spread: -1 (Flavio Cobolli wins 1 more games)

📊 Games Spread Probability Distribution

Distribution

Probability of each games spread outcome

Probability distribution chart for games spread in Alexander Blockx versus Flavio Cobolli. Positive values indicate Alexander Blockx winning more games, negative values indicate Flavio Cobolli winning more games.
Cumulative Probability (CDF)

Probability of spread ≤ X

Cumulative distribution function chart for games spread in Alexander Blockx versus Flavio Cobolli. The curve shows the cumulative probability for each spread threshold.

Aces and Double Faults Predictions

The aces prediction calls for 16.18 total aces in the match, with predicted aces split 9.34 for Blockx and 6.95 for Cobolli. The double faults prediction is 8.07 expected double faults, split 4.06 for Blockx and 4.02 for Cobolli. On hard courts, which produce moderate ace counts compared with grass or clay, these predicted aces fit the surface profile. Because neither player has a substantially higher serve rating, the per player ace projections show Blockx with a small advantage in serve volume, 9.34 versus 6.95.
🎯
Expected Total Aces 16.2 Most likely: 14 aces Per player: Alexander Blockx 9.3 + Flavio Cobolli 7.0
Expected Total Double Faults 8.1 Most likely: 7 double faults Per player: Alexander Blockx 4.1 + Flavio Cobolli 4.0

🎯 Aces Probability Distribution

Distribution

Probability of each ace count outcome

Probability distribution chart for total aces in Alexander Blockx versus Flavio Cobolli. 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 Alexander Blockx versus Flavio Cobolli. The curve shows the cumulative probability for each aces threshold.

🎯 Aces by Player

Alexander Blockx

Expected 9.3 aces

Probability distribution for the aces Alexander Blockx serves in this match.
Flavio Cobolli

Expected 7.0 aces

Probability distribution for the aces Flavio Cobolli serves in this match.

Double Faults Probability Distribution

Distribution

Probability of each double fault count outcome

Probability distribution chart for double faults in Alexander Blockx versus Flavio Cobolli. The chart shows the predicted probability for each double-fault count.
Cumulative Probability (CDF)

Probability of double faults ≤ X

Cumulative distribution function chart for double faults in Alexander Blockx versus Flavio Cobolli. The curve shows the cumulative probability for each double faults threshold.

Double Faults by Player

Alexander Blockx

Expected 4.1 double faults

Probability distribution for the double faults Alexander Blockx serves in this match.
Flavio Cobolli

Expected 4.0 double faults

Probability distribution for the double faults Flavio Cobolli serves in this match.

🎯 Match Format Predictions

Tiebreak Likelihood

Probability that any tiebreak is played in this match

44.0% Predicted: No tiebreak

Exact Score Distribution BO5

Probability of each set-by-set outcome (Alexander Blockx's perspective)

0-3 Most likely set score (25.4%)
Probability distribution of the final set score from Alexander Blockx's perspective. Format: BO5.

Final Prediction

Cobolli’s edge is rooted in overall strength, reflected in his higher rank and Elo and a stronger surface index. Watch the early serve exchanges and return games, as Blockx’s superior return index and recent form could swing tight service games and decide whether this stays close or tilts Cobolli’s way.

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