ATP 500 · Round of 32 · Tokyo
Final score
Playing styles and historical archetype record
Cilic
🤖 Servebot
Paire
💥 Big Hitter
Matchup analysis
The 🤖 Servebot archetype wins about 55% vs 💥 Big Hitter across 1,398 matches in our dataset. That is a small stylistic lean for Marin Cilic here.
Servebot players often use high ace rate and dominant service to pressure the big hitter's power groundstrokes. On Hard, that can swing with conditions.
Hypothetical if you put 1u on each player to win every one of their matches · Last 90 days
| Metric | Cilic | Paire |
|---|---|---|
| W-L | 4–6 | - |
| Avg odds | 1.95 | - |
| ROI % | -27.5% | - |
| Units P/L | -2.75u | - |
Prior meetings and scores · 6 career meetings
5
Cilic
1
Paire
Feb 12, 2020
Dubai
H Hard · Round of 32
Jan 23, 2020
Australian Open
H Hard · Round of 64
May 11, 2018
ATP Masters 1000 Rome
C Clay · Round of 16
Feb 8, 2017
Rotterdam
H Hard · Round of 32
Oct 5, 2016
Tokyo
H Hard · Round of 32
Feb 18, 2016
Marseille
H Hard · Semifinals
Serve and return comparison · Last 90 days
| Stat | Cilic | Paire |
|---|---|---|
| Serve | ||
| 1st Serve In % | 60.9% | 0% |
| 1st Serve Pts Won % | 71.5% | 0% |
| 2nd Serve Pts Won % | 50.8% | 0% |
| Aces / Match | 6 | 0 |
| Double Faults / Match | 2.5 | 0 |
| Break Points Saved % | 56.6% | 0% |
| Return | ||
| 1st Return Pts Won % | 28.5% | 0% |
| 2nd Return Pts Won % | 46.6% | 0% |
| BP Converted % | 40% | 0% |
| Rally | ||
| Winners / Match | 18.4 | 0 |
| Unforced Errors / Match | 27.6 | 0 |
| Net Pts Won % | 76.6% | 0% |
| Total Pts Won % | 49.6% | 0% |
| Surface (Hard) | ||
| Hard Win % | 0% | 0% |
Last five matches per player
Matchup overview
Marin Cilic and Benoit Paire face off in the Round of 32 at Tokyo on Hard.
Marin Cilic leads 5-1 over 6 previous meetings.
Marin Cilic is 2-3 over the last 5 matches. Latest result: L vs Ugo Humbert (Jun 16).
Benoit Paire is 0-5 over the last 5 matches. Latest result: L vs Benjamin Bonzi (Jan 27).
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