Cincinnati OH, U.S.A. Hard Masters 1000 Round of 128

Alex Michelsen vs Jesper de Jong: AI Prediction | Games, Spread, Aces & Double Faults

Alex Michelsen

Rank: #42
57%
VS

Jesper de Jong

Rank: #105
43%
Expected Total Games: 25.6
Predicted Winner: Alex Michelsen

Why the Model Favors Alex Michelsen

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

Recent form +5.6 Alex Michelsen
Overall strength +2.4 Alex Michelsen
Serve & return game +2.4 Jesper de Jong
Age +1.5 Alex Michelsen
Surface fit +0.9 Alex Michelsen

Starting from an even matchup, these factors move the model to 57% for Alex Michelsen. Computed with gradient-based attribution on our neural network, not editorial opinion. How to read this →

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Player Metrics

Alex Michelsen

Form Index: 41.9
ELO Rating: 1721.4
Glicko2 Rating: 1773.2
Current Fatigue (minutes): 0.0
Surface Strength:
Hard: 33.8
Clay: 18.6
Grass: 10.0
Serve Rating: 96.4
Return Rating: 88.8

Jesper de Jong

Form Index: 37.4
ELO Rating: 1648.7
Glicko2 Rating: 1577.4
Current Fatigue (minutes): 0.0
Surface Strength:
Hard: 16.8
Clay: 22.4
Grass: 9.0
Serve Rating: 95.1
Return Rating: 86.3

Recent Matches

Alex Michelsen

Jesper de Jong

Head-to-Head (Last 2 Seasons)

0
Alex Michelsen
vs
0
Jesper de Jong
Hard
0 - 0
Clay
0 - 0
Grass
0 - 0

Key Prediction Insights

Cincinnati, Ohio. Round of 128 on hard courts at a masters 1000 event. The model favors Alex Michelsen to win with a 57.24% probability over Jesper de Jong at 42.76%. The match is expected to be modest in length, with a predicted total of about 25.58 games.

Match Analysis

The model’s edge for Michelsen comes mainly from recent form, supported by modest advantages in overall strength, age, and surface fit. Michelsen is the higher ranked player at 42 versus de Jong at 105. His form index reads 41.86 against de Jong’s 37.41, and his Elo is 1721.43 compared with 1648.71. Fatigue is neutral for both players with zero minutes on court in this event. Michelsen’s surface strength index on hard is 33.84, noticeably above de Jong’s 16.80, which helps explain the model’s small surface fit edge toward Michelsen. The model also awards 2.4 percentage points to de Jong for serve and return game, despite the raw serve and return indices being very similar. Michelsen’s mean serve index is 96.39 and his mean return index is 88.81. De Jong posts a mean serve index of 95.05 and a mean return index of 86.33. Neither player shows a large gap in serving or returning by these metrics. Over the last three matches Michelsen has two wins and a loss at Toronto on hard courts, including a straight sets win over Francisco Cerundolo and a three set win over Jan-Lennard Struff before a recent 1-2 loss to Daniel Merida. De Jong’s recent form has come on clay, with one win and two losses, including straight set defeats to Sebastian Baez and a three set loss to Timofey Skatov.

Total Games Predictions

🎾
Expected Total Games in Match 25.6 Most likely outcome: 25 games

📊 Total Games Probability Distribution

Distribution

Probability of each total games outcome

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

Games Spread Predictions

📈
Expected Games Spread (Alex Michelsen - Jesper de Jong) +0.9 Most likely spread: +1 (Alex Michelsen wins 1 more games)

📊 Games Spread Probability Distribution

Distribution

Probability of each games spread outcome

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

Probability of spread ≤ X

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

Aces and Double Faults Predictions

The aces prediction for this hard court match is modest, with predicted aces at about 13.84 total. The double faults prediction shows expected double faults around 7.9 for the match. Hard courts tend to produce a balanced ace count, and since neither player has a significantly higher serve rating, the predicted aces should be split rather evenly.
🎯
Expected Total Aces 13.8 Most likely: 13 aces
Expected Total Double Faults 7.9 Most likely: 7 double faults

🎯 Aces Probability Distribution

Distribution

Probability of each ace count outcome

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

🎯 Match Format Predictions

Tiebreak Likelihood

Probability that any tiebreak is played in this match

39.0% Predicted: No tiebreak

Exact Score Distribution BO3

Probability of each set-by-set outcome (Alex Michelsen's perspective)

2-0 Most likely set score (35.6%)
Probability distribution of the final set score from Alex Michelsen's perspective. Format: BO3.

Final Prediction

Michelsen’s lead is driven most by recent form, augmented by higher ranking and a better hard court fit. The key factor to watch will be how de Jong’s serve and return game performs relative to the model’s expectation, since that component narrows the gap in this matchup.

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