Indian Wells CA, U.S.A. Hard Masters 1000 Round of 128

Zachary Svajda vs Marin Cilic: AI Prediction | Games, Spread, Aces & Double Faults

Zachary Svajda

Rank: #98
61%
VS

Marin Cilic

Rank: #45
39%
Expected Total Games: 23.4
Predicted Winner: Zachary Svajda

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Ranking Trends

Zachary Svajda

  • Current #98 2026-02-23
  • 3 mo ago #143 2025-12-01
  • 6 mo ago #145 2025-08-25
  • 9 mo ago #228 2025-05-26
  • 12 mo ago #187 2025-03-03

Marin Cilic

  • Current #45 2026-02-23
  • 3 mo ago #75 2025-12-01
  • 6 mo ago #62 2025-08-25
  • 9 mo ago #104 2025-05-26
  • 12 mo ago #142 2025-03-03

Player Metrics

Zachary Svajda

Form Index: 45.3
ELO Rating: 524.3
Glicko2 Rating: 1553.2
Current Fatigue (minutes): 0.0
Surface Strength:
Hard: 3.3
Clay: 2.8
Grass: 3.6
Serve Rating: 59.0
Return Rating: 55.5

Marin Cilic

Form Index: 46.9
ELO Rating: 1046.6
Glicko2 Rating: 1550.7
Current Fatigue (minutes): 0.0
Surface Strength:
Hard: 10.2
Clay: 5.9
Grass: 8.9
Serve Rating: 97.2
Return Rating: 87.1

Recent Matches

Zachary Svajda
❌ ✅ ❌ ✅ ✅

  • Last Match: ❌ vs Sho Shimabukuro (0-1) hard Acapulco 16 min
  • 2nd Last Match: ✅ vs Andres Martin (1-0) hard Acapulco 44 min
  • 3rd Last Match: ❌ vs Frances Tiafoe (1-2) hard Delray Beach 141 min
  • 4th Last Match: ✅ vs Aleksandar Kovacevic (2-0) hard Delray Beach 88 min
  • 5th Last Match: ✅ vs Yibing Wu (2-0) hard Delray Beach 78 min

Marin Cilic
❌ ❌ ✅ ✅ ✅

Head-to-Head (Last 2 Seasons)

0
Zachary Svajda
vs
0
Marin Cilic
Hard
0 - 0
Clay
0 - 0
Grass
0 - 0

Key Prediction Insights

At Indian Wells (CA, U.S.A.), round of 128 on outdoor hard courts at a masters_1000 event, the model gives Zachary Svajda the edge over Marin Cilic. Svajda is the projected winner with a 61.04% probability to Cilic’s 38.96%, and the match is expected to contain about 23.37 total games.

Match Analysis

Svajda (rank 98) enters with a form index of 45.29, an Elo of 524.25 and no accumulated fatigue in the event. His surface strength index on hard is 3.35, with a mean serve index of 59.03 and a mean return index of 55.48. Cilic (rank 45) posts a similar recent form index (46.88) but carries a substantially higher Elo (1046.60) and a stronger surface index (10.25). Fatigue is listed at 0.0 for both players. The mean serve index gap is large — roughly a 38-point advantage to Cilic — and his mean return index is similarly higher by about 31 points; both differences are material and worth watching. Looking at recent results, Svajda is coming off mixed hard-court outings: a win over Andres Martin in Acapulco but a loss to Sho Shimabukuro and a competitive three-set loss to Frances Tiafoe earlier. Cilic’s last three hard-court matches show one straight-sets win over Jack Pinnington Jones and defeats to Brandon Nakashima and Taylor Fritz. Neither player shows tournament fatigue, so Saturday’s match should reflect current form and their respective serving/return profiles rather than wear-and-tear.

Total Games Predictions

🎾
Expected Total Games in Match 23.4 Most likely outcome: 23 games

📊 Total Games Probability Distribution

Distribution

Probability of each total games outcome

Cumulative Probability (CDF)

Probability of total games ≤ X

Games Spread Predictions

📈
Expected Games Spread (Zachary Svajda - Marin Cilic) +0.6 Most likely spread: 0 (even number of games won)

📊 Games Spread Probability Distribution

Distribution

Probability of each games spread outcome

Cumulative Probability (CDF)

Probability of spread ≤ X

Aces and Double Faults Predictions

The aces prediction for the match is about 12.95 total, while the expected double faults are around 4.17. On a medium-paced hard court that tends to balance serve and return, the predicted aces reflect a moderate tally rather than an outsize number. Given Cilic’s much higher serve rating, he is likely to contribute a disproportionate share of the predicted aces.
🎯
Expected Total Aces 12.9 Most likely: 12 aces
❌
Expected Total Double Faults 4.2 Most likely: 4 double faults

🎯 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

New to probability charts? Learn how to interpret distribution and CDF curves

Final Prediction

The model’s edge for Svajda (61%) rests on the composite metrics in the dataset despite Cilic’s superior serve/return indices and higher Elo. The key factor to watch will be how Svajda handles Cilic’s serve — neutralizing it on return would be decisive.

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