Coco Gauff's Form Tests Elena Rybakina's Edge
Coco Gauff's longer winning run gives her a narrow model edge over Elena Rybakina at the US Open.
The market moved against the form
8 points. That is how far the market has moved toward Elena Rybakina since Coco Gauff began at 56%. The shift runs against Gauff's immediate results on these courts and gives this meeting its central tension. Reputation is pulling one way, while recent form points another.
The ranking comparison explains part of Rybakina's appeal. Gauff is No. 6 and Rybakina is No. 4, which makes the gap at the top of the draw clear. Rybakina has also reached the better career ranking. That history matters, particularly in a match between players who have both handled the late stages of a major event.
Still, rankings describe a broader period than the last stretch of matches. Gauff arrives without a recent defeat, while Rybakina arrives after a strong run of her own and a recent win over this opponent. Neither player is trying to find rhythm. The question is whether Gauff can make her current level matter more than the ranking order. That is a demanding task. It requires her to keep the match centered on the form she has shown throughout this tournament.
Model win probability
Article snapshot, Sep 10, 2026 · Not live
The Court Shark model's chance for each player to win this match.
| Player | Win probability | Recent form (last 10) |
|---|---|---|
| Coco Gauff | 55% | 10-0 |
| Elena Rybakina | 45% | 9-1 |
Gauff has the longer run
Gauff has gone 10-0 across her last ten matches. Her current streak has reached 12 matches, and that gives her a longer uninterrupted run than anyone else in this pairing. The hard courts have suited her throughout that spell. She has repeatedly found a way through matches without allowing one difficult moment to turn into a loss.
Rybakina's recent record is 9-1. She also carries a five-match winning run, so this is not a meeting between a player in form and one searching for answers. Her level has been high. The small difference is that Gauff has not recorded the recent loss that appears in Rybakina's record.
Both players were on court the day before this match. Each has played five matches in the last 14 days, leaving neither with a freshness advantage created by a long break. Match sharpness should be available from the opening games. Recovery will still matter, because both have had a demanding route through the event. Gauff's edge is the cleaner sequence of wins. Rybakina's case is that her recent run includes the matchup result Gauff most needs to reverse.
Toronto gives Rybakina a useful reference
The overall head-to-head stands at 1-1. It is also 1-1 on hard courts, leaving neither player with a lasting edge from their previous meetings. That balance matters. The pairing has already produced wins for both, so a single past result cannot settle the argument.
Rybakina won the latest meeting in Toronto. Gauff took the opening set, but Rybakina recovered and controlled the remainder of the contest. The result offers a useful reference rather than a fixed script. It shows that Rybakina can reset after a slow start and keep Gauff from carrying early momentum through the full match.
Gauff's response has to be sustained rather than brief. An early advantage would help, but she will need to continue pressing once Rybakina settles into her service games and begins to dictate more of the exchanges. That is the demanding part. Rybakina has recent evidence that she can absorb an initial setback in this matchup. Gauff has stronger current form, yet the Toronto meeting means that form must hold up through the match's later momentum swings.
What the model says
The model gives Gauff a 55% chance and Rybakina a 45% chance. It makes Gauff the narrow favorite, with her longer winning run outweighing the difference in ranking. This remains close. Rybakina's recent result in Toronto leaves very little room for Gauff's level to dip.
The market consensus has Gauff at 48% and Rybakina at 52%. That consensus favors Rybakina, creating a clear disagreement with the model on a match shaped by form against reputation. The gap is real. Gauff's unbeaten recent stretch is the main reason the model sees the matchup differently.
The winner moves on to the next match in the US Open draw. Gauff would carry a longer active run forward, while Rybakina would bring another confirmation that her ranking and recent matchup success still set the standard. There is no wide margin here. The decisive passages may come after one player has already seized momentum. Whoever handles those resets better will be in the stronger position to extend a strong fortnight.
Explore this matchup
Compare both players side by side: recent results, head-to-head and the model.
Player comparison
Article snapshot, Sep 10, 2026 · Not live
Each measure on the same scale: recent wins, wins on this surface, head-to-head wins and the model's chance.
| Measure | Coco Gauff | Elena Rybakina |
|---|---|---|
| Wins, last 10 matches | 100% (10-0) | 90% (9-1) |
| Wins, last 10 on hard | 100% (10-0) | 90% (9-1) |
| Head-to-head wins | 50% (1-1) | 50% (1-1) |
| Model win probability | 55% | 45% |
Head-to-head by surface
Article snapshot, Sep 10, 2026 · Not live
Coco Gauff and Elena Rybakina have met 2 times. The record is level at 1-1.
| Surface | Coco Gauff | Elena Rybakina |
|---|---|---|
| All surfaces | 1 | 1 |
| Hard | 1 | 1 |
Model and market on Coco Gauff
Article snapshot, Sep 10, 2026 · Not live
Coco Gauff's chance as the market consensus priced it from the opening line, next to the Court Shark model.
| Reading | Coco Gauff | Elena Rybakina |
|---|---|---|
| Opening consensus | 56% | 44% |
| Latest consensus | 48% | 52% |
| Court Shark model | 55% | 45% |
Court Shark win probabilities are statistical estimates, not advice. No outcome is guaranteed.
Methodology and sources
How we build model estimates and where our information comes from.
Model estimates and observed results
The win probabilities come from the Court Shark match model, trained on completed ATP and WTA matches. A probability says how often a player in this position wins, not that one result will happen. The model's number for a match is recorded before play and never changed afterwards, so it can be checked against the result.
Sources and citations
Results, scores, rankings and serve statistics come from the Court Shark match database. The market consensus is the pre-match price for each player converted to a probability. Figures in this article are a snapshot taken when it was written.
Limitations
Serve and return rates count only matches with full serve statistics, which are sparser for Challenger and lower-level events, and the tour average they are compared with covers every year on record. Head-to-head records count every meeting with a recorded winner, walkovers and retirements included. Charts and tables are a snapshot and do not update after publication.
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