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

Reilly Opelka vs Ethan Quinn: AI Prediction | Games, Aces & Double Faults

Reilly Opelka

Rank: #69
61%
VS

Ethan Quinn

Rank: #72
39%
Expected Total Games: 25.3
Predicted Winner: Reilly Opelka

Player Metrics

Reilly Opelka

Form Index: 44.3
ELO Rating: 872.5
Glicko2 Rating: 1537.3
Current Fatigue (minutes): 0.0
Surface Strength:
Hard: 7.5
Clay: 6.2
Grass: 8.1
Serve Rating: 95.8
Return Rating: 12.2

Ethan Quinn

Form Index: 38.8
ELO Rating: 822.5
Glicko2 Rating: 1536.2
Current Fatigue (minutes): 0.0
Surface Strength:
Hard: 7.8
Clay: 7.7
Grass: 6.1
Serve Rating: 96.4
Return Rating: 88.3

Recent Matches

Reilly Opelka

  • Last Match: vs Alejandro Davidovich Fokina (2-3) hard Australian Open 174 min
  • 2nd Last Match: vs Nicolai Budkov Kjaer (3-0) hard Australian Open 174 min
  • 3rd Last Match: vs Tommy Paul (0-2) hard Adelaide 71 min
  • 4th Last Match: vs Alexei Popyrin (2-0) hard Adelaide 83 min
  • 5th Last Match: vs Kamil Majchrzak (1-2) hard Brisbane 149 min

Ethan Quinn

  • Last Match: vs Rafael Jodar (0-2) hard Delray Beach 78 min
  • 2nd Last Match: vs Marin Cilic (0-2) hard Dallas 97 min
  • 3rd Last Match: vs Trevor Svajda (2-0) hard Dallas 106 min
  • 4th Last Match: vs Jakub Mensik (0-3) hard Australian Open 174 min
  • 5th Last Match: vs Hubert Hurkacz (3-0) hard Australian Open 174 min

Head-to-Head (Last 2 Seasons)

0
Reilly Opelka
vs
0
Ethan Quinn
Hard
0 - 0
Clay
0 - 0
Grass
0 - 0

Key Prediction Insights

At Indian Wells (hard, Round of 128, Masters 1000), Reilly Opelka meets Ethan Quinn in a matchup between two big servers and contrasting return profiles. The model favors Reilly Opelka to win (60.55% vs 39.45% for Quinn) with an expected total of about 25.3 games in the match.

Match Analysis

Opelka enters ranked 69 with a form index of 44.26 and an Elo of 872.47; Quinn is close in the rankings at 72 with a form index of 38.81 and a lower Elo of 822.51. Neither player carries tournament fatigue into this encounter. Surface strength indices on hard court are virtually identical (Opelka 7.48, Quinn 7.78). Serve metrics are similar — Opelka’s mean serve index is 95.77 and Quinn’s 96.38, so there’s no meaningful serve-index gap to note. By contrast, the return profile is a clear divider: Quinn’s mean return index sits at 88.30 versus Opelka’s 12.15, a large difference that will be central to how rallies develop. Recent form tells a complementary story. Opelka has two wins and one tight five-set loss in his last three hard‑court outings, including a long Australian Open match (174 minutes) versus Alejandro Davidovich Fokina and a straight-sets victory earlier in the same event. Quinn’s last three matches show one win and two straight-set defeats; his victories and losses have also come on hard courts, with recent defeats to higher-ranked opponents and a 78– to 97–minute range in match durations. These patterns underline Opelka’s marginal edge in resilience and Elo, while Quinn’s return ability could create pressure in short windows.

Total Games Predictions

🎾
Expected Total Games in Match 25.3 Most likely outcome: 25 games

📊 Total Games Probability Distribution

Distribution

Probability of each total games outcome

Cumulative Probability (CDF)

Probability of total games ≤ X

Aces and Double Faults Predictions

The aces prediction for this match is high: the model forecasts about 19.38 total aces and expected double faults around 5.72. On medium‑pace hard courts, that predicted aces total aligns with two big servers producing frequent free points but not the extreme ace counts seen on grass. Because serve ratings are nearly identical, neither player alone fully accounts for the high predicted aces; instead, both servers should contribute. The double faults prediction suggests moderate risk on second serves, typical for an aggressive serving matchup.
🎯
Expected Total Aces 19.4 Most likely: 19 aces
Expected Total Double Faults 5.7 Most likely: 5 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

Final Prediction

Opelka’s higher Elo and marginally better form give him the edge in the model, but the real hinge will be how Quinn’s elite return index converts opportunities against two very strong servers. Keep an eye on return games early — break opportunities there will likely decide the contest.

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