Bastad Sweden Clay Atp 250 Round of 32

Dalibor Svrcina vs Grigor Dimitrov: AI Prediction | Games, Spread, Aces & Double Faults

Dalibor Svrcina

Rank: #112
30%
VS

Grigor Dimitrov

Rank: #146
70%
Expected Total Games: 21.9
Predicted Winner: Grigor Dimitrov

Why the Model Favors Grigor Dimitrov

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

Recent form +10.4 Grigor Dimitrov
Serve & return game +5.3 Grigor Dimitrov
Age +4.2 Dalibor Svrcina
Surface fit +3.4 Grigor Dimitrov
Overall strength +2.5 Grigor Dimitrov

Starting from an even matchup, these factors move the model to 70% for Grigor Dimitrov. Computed with gradient-based attribution on our neural network — not editorial opinion. How to read this →

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Player Metrics

Dalibor Svrcina

Form Index: 36.4
ELO Rating: 1573.9
Glicko2 Rating: 1619.0
Current Fatigue (minutes): 0.0
Surface Strength:
Hard: 18.6
Clay: 13.6
Grass: 6.7
Serve Rating: 94.3
Return Rating: 96.1

Grigor Dimitrov

Form Index: 41.2
ELO Rating: 1659.3
Glicko2 Rating: 1640.0
Current Fatigue (minutes): 0.0
Surface Strength:
Hard: 22.3
Clay: 12.3
Grass: 25.8
Serve Rating: 95.2
Return Rating: 85.7

Recent Matches

Dalibor Svrcina

Grigor Dimitrov

Head-to-Head (Last 2 Seasons)

0
Dalibor Svrcina
vs
0
Grigor Dimitrov
Hard
0 - 0
Clay
0 - 0
Grass
0 - 0

Key Prediction Insights

In the opening round at Båstad on clay, Dalibor Svrcina meets Grigor Dimitrov in a Round of 32 clash at this 250-level event in Sweden. The model backs Dimitrov to advance — 69.53% chance to win versus 30.47% for Svrcina — with a relatively short match expected: about 21.94 total games.

Match Analysis

The model’s edge for Dimitrov is driven primarily by recent form (+10.4 percentage points) and his combined serve/return profile (+5.3). Dimitrov’s form index (41.22) is healthier than Svrcina’s (36.36), and his Elo rating (1659.28) sits above Svrcina’s (1573.89), supporting the notion that Dimitrov has been the stronger player in recent results. Both arrive fresh with zero minutes of tournament fatigue recorded. Surface-fit is flagged by the model (+3.4 toward Dimitrov), even though both players show modest clay-specific indexes; the model is picking up matchup subtleties beyond raw surface scores. Looking at the raw skills, serve numbers are very close (Svrcina 94.27 vs Dimitrov 95.18), so there is no meaningful serving gap to exploit on paper. By contrast, Svrcina holds a clear advantage in return (96.11 vs 85.75) — a >10-point difference that suggests he can pressure Dimitrov’s service games on clay. Recent matches underline the contrast: Svrcina is 1–2 in his last three (one win at Roland Garros qualifying, two losses including Wimbledon), while Dimitrov is 2–1 (a run to the latter stages at Wimbledon before a recent loss).

Total Games Predictions

🎾
Expected Total Games in Match 21.9 Most likely outcome: 21 games

📊 Total Games Probability Distribution

Distribution

Probability of each total games outcome

Probability distribution chart for total games in Dalibor Svrcina versus Grigor Dimitrov. 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 Dalibor Svrcina versus Grigor Dimitrov. The curve rises from 0% to 100%, showing the cumulative probability for each games total threshold.

Games Spread Predictions

📈
Expected Games Spread (Dalibor Svrcina - Grigor Dimitrov) -1.8 Most likely spread: -2 (Grigor Dimitrov wins 2 more games)

📊 Games Spread Probability Distribution

Distribution

Probability of each games spread outcome

Probability distribution chart for games spread in Dalibor Svrcina versus Grigor Dimitrov. Positive values indicate Dalibor Svrcina winning more games, negative values indicate Grigor Dimitrov winning more games.
Cumulative Probability (CDF)

Probability of spread ≤ X

Cumulative distribution function chart for games spread in Dalibor Svrcina versus Grigor Dimitrov. The curve shows the cumulative probability for each spread threshold.

Aces and Double Faults Predictions

The aces prediction sits at a modest 6.82 for the match, and the expected double faults are 7.34. On slow clay, predicted aces are suppressed and expected double faults tend to rise due to longer points and physical strain. Neither player has a significantly higher serve rating to push the predicted aces much higher, so the predicted aces and expected double faults reflect a typical clay duel.
🎯
Expected Total Aces 6.8 Most likely: 6 aces
Expected Total Double Faults 7.3 Most likely: 7 double faults

🎯 Aces Probability Distribution

Distribution

Probability of each ace count outcome

Probability distribution chart for total aces in Dalibor Svrcina versus Grigor Dimitrov. 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 Dalibor Svrcina versus Grigor Dimitrov. 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 Dalibor Svrcina versus Grigor Dimitrov. 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 Dalibor Svrcina versus Grigor Dimitrov. 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

35.0% Predicted: No tiebreak

Exact Score Distribution BO3

Probability of each set-by-set outcome (Dalibor Svrcina's perspective)

0-2 Most likely set score (38.9%)
Probability distribution of the final set score from Dalibor Svrcina's perspective. Format: BO3.

Final Prediction

Dimitrov’s lead comes mainly from recent form and a slight overall strength edge (Elo), which together tilt the model toward him. The key factor to watch is Svrcina’s high return index — if he can convert that pressure into breaks early, he can flip the script despite the pre-match probabilities.

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