WTA 1000 · Round of 16 · Miami
Final score & match stats
Playing styles and historical archetype record
Stephens
⚙️ Grinder
Muguruza
🤖 Servebot
Matchup analysis
The 🤖 Servebot archetype wins about 60% vs ⚙️ Grinder across 1,639 matches in our dataset. That is a small stylistic lean for Garbine Muguruza here.
Servebot players often use high ace rate and dominant service to pressure the grinder's relentless consistency. 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 | Stephens | Muguruza |
|---|---|---|
| W-L | 1–4 | - |
| Avg odds | 3.74 | - |
| ROI % | -68.2% | - |
| Units P/L | -3.41u | - |
Small sample: ROI stabilizes with more matches; fewer than 10 in the window is noisy.
Prior meetings and scores · 4 career meetings
2
Stephens
2
Muguruza
Sep 14, 2020
Rome
C Clay · Round of 64
May 27, 2019
Roland Garros
C Clay · Round of 16
Mar 19, 2018
Miami
H Hard · Round of 16
Sep 28, 2015
Wuhan
H Hard · Round of 32
Serve and return comparison · Last 90 days
| Stat | Stephens | Muguruza |
|---|---|---|
| Serve | ||
| 1st Serve In % | 65.6% | 0% |
| 1st Serve Pts Won % | 48.7% | 0% |
| 2nd Serve Pts Won % | 46.8% | 0% |
| Aces / Match | 0.8 | 0 |
| Double Faults / Match | 2.3 | 0 |
| Break Points Saved % | 56.2% | 0% |
| Return | ||
| 1st Return Pts Won % | 25.1% | 0% |
| 2nd Return Pts Won % | 39.4% | 0% |
| BP Converted % | 49.1% | 0% |
| Rally | ||
| Winners / Match | 0 | 0 |
| Unforced Errors / Match | 0 | 0 |
| Net Pts Won % | 0% | 0% |
| Total Pts Won % | 40.1% | 0% |
| Surface (Hard) | ||
| Hard Win % | 0% | 0% |
Last five matches per player
Matchup overview
Sloane Stephens and Garbine Muguruza face off in the Round of 16 at Miami on Hard.
Sloane Stephens leads 2-2 over 4 previous meetings.
Sloane Stephens is 1-4 over the last 5 matches. Latest result: L vs Sara Bejlek (May 25).
Garbine Muguruza is 0-5 over the last 5 matches. Latest result: L vs Linda Noskova (Jan 30).
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