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

Matteo Arnaldi vs Mackenzie McDonald: AI Prediction | Games, Spread, Aces & Double Faults

Matteo Arnaldi

Rank: #88
63%
VS

Mackenzie McDonald

Rank: #119
37%
Expected Total Games: 23.2
Predicted Winner: Matteo Arnaldi

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Player Metrics

Matteo Arnaldi

Form Index: 25.9
ELO Rating: 865.5
Glicko2 Rating: 1633.0
Current Fatigue (minutes): 0.0
Surface Strength:
Hard: 6.7
Clay: 8.6
Grass: 6.6
Serve Rating: 79.0
Return Rating: 50.6

Mackenzie McDonald

Form Index: 49.1
ELO Rating: 516.8
Glicko2 Rating: 1548.3
Current Fatigue (minutes): 123.0
Surface Strength:
Hard: 5.2
Clay: 4.1
Grass: 5.1
Serve Rating: 97.8
Return Rating: 89.9

Recent Matches

Matteo Arnaldi

Mackenzie McDonald

  • Last Match: vs Trevor Svajda (2-0) hard Indian Wells 67 min
  • 2nd Last Match: vs Vitaliy Sachko (2-0) hard Indian Wells 56 min
  • 3rd Last Match: vs Elias Ymer (1-2) hard Acapulco 160 min
  • 4th Last Match: vs Sebastian Korda (0-2) hard Delray Beach 88 min
  • 5th Last Match: vs Liam Draxl (1-2) hard Australian Open 174 min

Head-to-Head (Last 2 Seasons)

0
Matteo Arnaldi
vs
0
Mackenzie McDonald
Hard
0 - 0
Clay
0 - 0
Grass
0 - 0

Key Prediction Insights

At Indian Wells in California, Matteo Arnaldi and Mackenzie McDonald meet in the round of 128 on hard courts at a Masters 1000 event. The model favors Matteo Arnaldi to win (62.72%) over Mackenzie McDonald (37.28%), with an expected total of about 23.2 games in the match.

Match Analysis

Arnaldi comes into the match ranked 88 with an Elo of 865.5 and a form index of 25.9; his surface strength index on hard courts is 6.70 and his cumulative tournament fatigue is listed at 0.0. McDonald is ranked 119 with a lower Elo (516.8) but a stronger recent form index (49.1), a surface strength index of 5.19 and measurable fatigue of 123 minutes from previous matches at this event. Notable mismatches in the serving and returning metrics: McDonald’s mean serve index (97.78) is far higher than Arnaldi’s (78.97), and his mean return index (89.95) is also substantially above Arnaldi’s (50.64). Those gaps (both >5 points) suggest McDonald can produce more free points on serve while also posing a serious threat on return. Recent results underline the contrast. Arnaldi’s last three matches show mixed outcomes: a heavy 0-3 loss to Andrey Rublev at the Australian Open (174 minutes), a hard-fought win over Hugo Gaston in Brisbane (2-1, 152 minutes) and a straight-sets loss to Jacob Fearnley in Paris. McDonald has two straight, quick wins at Indian Wells (Trevor Svajda in 67 minutes and Vitaliy Sachko in 56 minutes) after a three-set defeat to Elias Ymer in Acapulco (160 minutes). Those shorter wins at the venue explain McDonald’s tournament minutes and his readiness despite the accumulated 123 minutes of court time.

Total Games Predictions

🎾
Expected Total Games in Match 23.2 Most likely outcome: 23 games

📊 Total Games Probability Distribution

Distribution

Probability of each total games outcome

Probability distribution chart for total games in Matteo Arnaldi versus Mackenzie McDonald. 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 Matteo Arnaldi versus Mackenzie McDonald. The curve rises from 0% to 100%, showing the cumulative probability for each games total threshold.

Games Spread Predictions

📈
Expected Games Spread (Matteo Arnaldi - Mackenzie McDonald) -0.5 Most likely spread: -1 (Mackenzie McDonald wins 1 more games)

📊 Games Spread Probability Distribution

Distribution

Probability of each games spread outcome

Probability distribution chart for games spread in Matteo Arnaldi versus Mackenzie McDonald. Positive values indicate Matteo Arnaldi winning more games, negative values indicate Mackenzie McDonald winning more games.
Cumulative Probability (CDF)

Probability of spread ≤ X

Cumulative distribution function chart for games spread in Matteo Arnaldi versus Mackenzie McDonald. The curve shows the cumulative probability for each spread threshold.

Aces and Double Faults Predictions

This match’s aces prediction is modest: the model expects about 6.72 aces combined and an expected double faults total near 4.11. On a medium-paced hard court, predicted aces sit between typical clay and grass levels; the surface rewards both power and return aggression. Given McDonald’s significantly higher serve rating, he is the likelier source of most of the predicted aces, while both players could contribute to the expected double faults total under pressure.
🎯
Expected Total Aces 6.7 Most likely: 6 aces
Expected Total Double Faults 4.1 Most likely: 4 double faults

🎯 Aces Probability Distribution

Distribution

Probability of each ace count outcome

Probability distribution chart for total aces in Matteo Arnaldi versus Mackenzie McDonald. 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 Matteo Arnaldi versus Mackenzie McDonald. 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 Matteo Arnaldi versus Mackenzie McDonald. 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 Matteo Arnaldi versus Mackenzie McDonald. The curve shows the cumulative probability for each double faults threshold.

Final Prediction

Arnaldi’s edge comes from higher Elo and a projection that he will manage key moments better across a relatively short match. The key factor to watch is McDonald’s serve-return combo: if his big serving converts free points and his elite return index fires, he can swing the match; if Arnaldi neutralizes those spikes, he should advance.

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