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# Coco Gauff's form overcomes Xinran Sun in Beijing
- URL: https://courtshark.ai/blog/coco-gauff-xinran-sun-beijing-result/
- Published: 2026-10-06T07:01:09.000Z
- Updated: 2026-10-06T07:01:08.000Z
- Description: Coco Gauff's 7-5 6-1 win over Xinran Sun matched the model, though Sun's form made the first set competitive.
- Author: Court Shark Desk
- Tags: results, Coco Gauff, Xinran Sun, BEIJING, Hard, WTA

In this analysis

1. [Key takeaway](#key-takeaway)
2. [Form met reputation in Beijing](#s-form-met-reputation-in-beijing)
3. [Gauff had the stronger recent body of work](#s-gauff-had-the-stronger-recent-body-of-work)
4. [Sun's run deserved more than a glance](#s-sun-s-run-deserved-more-than-a-glance)
5. [The hard-court matchup had competing signals](#s-the-hard-court-matchup-had-competing-signals)
6. [What the model says](#s-what-the-model-says)
7. [Explore this matchup](#explore)
8. [Methodology and sources](#methodology)

Key takeaway

Coco Gauff's 7-5 6-1 win matched a 90% model chance, but Xinran Sun's hard-court form was visible in a competitive first set.

Coco Gauff turned a competitive opening set into a straight-sets win over Xinran Sun in Beijing. The model gave Gauff a 90% chance before play, close to the market consensus.

## Form met reputation in Beijing

Coco Gauff's reputation was justified, but Xinran Sun's form made the opening set more complicated than the ranking gap suggested. [Coco Gauff](https://courtshark.ai/players/coco-gauff-10221103) won 7-5 6-1 on Monday, October 5, turning a close contest into a clear result. The match changed quickly.

Gauff arrived as No. 4, while [Xinran Sun](https://courtshark.ai/players/xinran-sun-12528006) was ranked No. 582\. That difference frames the expectation, yet it does not erase a player who had been winning consistently on hard courts. Sun had earned the right to test the favorite. Her early resistance showed why recent form deserved attention alongside reputation.

The first set supplied the match's tension. Gauff eventually found the cleaner path through the rallies and left Sun with less room to dictate. From there, the second set was one sided. The final score reflected Gauff's ability to take control after an initially tight passage, rather than an effortless afternoon from the first ball.

### What the model expected, and what happened

Article snapshot, Oct 6, 2026 · Not live

Coco Gauff won 7-5 6-1\. The model had made them the favorite.

__Pre-match model probability and the result__
| Player     | Model before the match | Result |
| ---------- | ---------------------- | ------ |
| Coco Gauff | 90%                    | Won    |
| Xinran Sun | 10%                    | Lost   |

Source: Court Shark model and match data.View data table

## Gauff had the stronger recent body of work

Gauff came into Beijing with a 9-1 record across her last ten completed matches. That run included wins over Jessica Pegula and Mirra Andreeva, opponents who require sustained quality rather than a brief burst. Her lone defeat came against Elena Rybakina. The overall picture was strong.

Her most recent result before this match also mattered. Gauff beat Camila Osorio in a three set Beijing contest, having recovered after dropping the middle set. That outcome did not promise another win, but it showed she had already handled pressure at this event. She was match ready. There was no sign of a long interruption.

The recent-form record shows a player winning in several ways. Gauff had straight-set victories in that sequence, but also came through a deciding set against Andreeva after losing the opener. That range helped explain why a tight first set against Sun did not become a wider problem. Once Gauff settled, her recent standard returned.

### Recent form

Article snapshot, Oct 6, 2026 · Not live

Each player's most recent completed matches, up to 10, oldest on the left.

__Recent results by player, oldest first__
| Player     | Date         | Result | Opponent             | Score          | Event        |
| ---------- | ------------ | ------ | -------------------- | -------------- | ------------ |
| Coco Gauff | Aug 22, 2026 | W      | Sara Bejlek          | 6-4 6-1        | Cincinnati   |
| Coco Gauff | Aug 22, 2026 | W      | Marta Kostyuk        | 6-2 6-2        | Cincinnati   |
| Coco Gauff | Aug 23, 2026 | W      | Jessica Pegula       | 6-2 6-4        | Cincinnati   |
| Coco Gauff | Sep 1, 2026  | W      | Zeynep Sonmez        | 6-3 6-4        | US Open      |
| Coco Gauff | Sep 3, 2026  | W      | Paula Badosa         | 6-4 7-6(5)     | US Open      |
| Coco Gauff | Sep 5, 2026  | W      | Cristina Bucsa       | 6-3 6-4        | US Open      |
| Coco Gauff | Sep 7, 2026  | W      | Iva Jovic            | 6-1 6-4        | US Open      |
| Coco Gauff | Sep 9, 2026  | W      | Mirra Andreeva       | 2-6 7-6(7) 6-2 | US Open      |
| Coco Gauff | Sep 11, 2026 | L      | Elena Rybakina       | 6-3 4-6 4-6    | US Open      |
| Coco Gauff | Oct 3, 2026  | W      | Camila Osorio        | 7-6(4) 4-6 6-2 | WTA Beijing  |
| Xinran Sun | May 7, 2026  | L      | Mia Ristic           | 2-6 1-6        | Istanbul 125 |
| Xinran Sun | May 13, 2026 | W      | Lucija Ciric Bagaric | 6-4 6-1        | W75 Zagreb   |
| Xinran Sun | May 14, 2026 | L      | Ella Seidel          | 2-6 0-6        | W75 Zagreb   |
| Xinran Sun | May 28, 2026 | W      | Mia Horvit           | 6-2 6-1        | W35 Bol      |
| Xinran Sun | May 29, 2026 | L      | Valentina Ivanov     | 4-6 3-6        | W35 Bol      |
| Xinran Sun | Sep 7, 2026  | W      | Thara Gowda          |                | US OPEN      |
| Xinran Sun | Sep 11, 2026 | W      | Chukwumelije Clarke  |                | US OPEN      |
| Xinran Sun | Sep 12, 2026 | W      | Anna Pushkareva      |                | WTA US Open  |
| Xinran Sun | Oct 1, 2026  | W      | Eva Lys              | 6-1 3-0 RET    | WTA Beijing  |
| Xinran Sun | Oct 3, 2026  | W      | Cristina Bucsa       | 6-4 7-5        | WTA Beijing  |

Source: Court Shark model and match data.View data table

## Sun's run deserved more than a glance

Sun's 7-3 mark across her last ten matches was a serious piece of pre-match context. She had also won eight of her last nine on hard courts, a result that made her far more competitive than her ranking alone implied. Her run was real. It gave the opening set a credible shape.

Two recent Beijing wins had brought Sun into this meeting with momentum. She beat Eva Lys after retirement, then followed it by defeating Cristina Bucsa in a complete match. Those results placed her on a five-match winning run. Home conditions can add comfort, although they cannot settle a contest by themselves.

Still, the level of the assignment changed sharply against Gauff. Sun's recent wins showed confidence and hard-court competence, while Gauff's sequence featured deeper opposition and more demanding matches. That distinction appeared after the first set. Sun had enough form to compete early, but Gauff had more proven answers when the match narrowed to decisive points.

## The hard-court matchup had competing signals

The hard-court records created an interesting contrast before the match. Gauff had won 25 of her last 32 tracked hard-court matches, while Sun had won five of six in her smaller sample. Both figures pointed toward capable recent work on the surface. Sample size mattered here.

Gauff's hard-court serving record offered a clearer edge. She held serve in 71% of those games, compared with 54% for Sun, giving the favorite a stronger platform to protect leads once she established them. Holding serve changes the rhythm of a match. It lets a player attack return games without chasing an immediate repair.

Sun's return figure was striking, with breaks in 54% of the hard-court sample. Yet that number came from only six matches with full statistics, whereas Gauff's sample covered 32\. The first set suggested Sun could apply pressure. The second showed Gauff could limit it over a longer stretch and make her own service games count.

## What the model says

The model gave Gauff a 90% chance and Sun a 10% chance before the match. Gauff's 7-5 6-1 win landed on the expected side of that forecast. It was not a flawless start. It was a convincing finish.

The market consensus had Gauff at 92% and Sun at 8%, and it stayed flat from the opening reading. That left only a small gap between the outside consensus and the model's view. Both saw Gauff as the clear favorite. Neither treated Sun's form as enough to reverse the matchup.

A longer run of top-level hard-court results from Sun would have changed the call. So would a weaker recent record from Gauff. Instead, Gauff paired a higher established level with strong current form. Sun made the case for attention in the first set, but could not sustain it.

### Model and market on Coco Gauff

Article snapshot, Oct 6, 2026 · Not live

Coco Gauff's chance as the market consensus priced it from the opening line, next to the Court Shark model.

__Coco Gauff's chance to win__
| Reading                | Coco Gauff | Xinran Sun |
| ---------------------- | ---------- | ---------- |
| Opening consensus      | 92%        | 8%         |
| Consensus at the start | 92%        | 8%         |
| Court Shark model      | 90%        | 10%        |

Source: Court Shark model and pre-match market consensus.View data table

## Explore this matchup

Compare both players side by side: recent results, surfaces, serve and return.

### Player comparison

Article snapshot, Oct 6, 2026 · Not live

Each measure on the same scale: recent wins, wins on this surface, head-to-head wins and the model's chance.

__Player comparison__
| Measure               | Coco Gauff | Xinran Sun |
| --------------------- | ---------- | ---------- |
| Wins, last 10 matches | 90% (9-1)  | 70% (7-3)  |
| Wins, last 10 on hard | 90% (9-1)  | 89% (8-1)  |
| Model win probability | 90%        | 10%        |

Source: Court Shark model and match data.View data table

### Win rate by surface

Article snapshot, Oct 6, 2026 · Not live

Share of matches won on each surface across each player's last 50 matches with full statistics.

__Win rate by surface__
| Surface | Coco Gauff | Xinran Sun |
| ------- | ---------- | ---------- |
| Hard    | 78% (25-7) | 83% (5-1)  |
| Clay    | 73% (8-3)  | 50% (3-3)  |
| Grass   | 71% (5-2)  | No matches |

Source: Court Shark model and match data.View data table

### Serve and return on hard courts

Article snapshot, Oct 6, 2026 · Not live

How often each player held serve and broke serve on hard over their last 50 matches with full statistics, against the tour's all-years average on the surface.

__Hold and break rates on hard__
| Measure             | Coco Gauff | Xinran Sun | Tour average, all years |
| ------------------- | ---------- | ---------- | ----------------------- |
| Service games held  | 71%        | 54%        | 63%                     |
| Return games broken | 41%        | 54%        | 37%                     |
| Matches in sample   | 32         | 6          |                         |

Source: Court Shark model and match data.View data table

### Model win probability

Article snapshot, Oct 6, 2026 · Not live

What the Court Shark model gave each player before the match started.

__Model win probability by player__
| Player     | Win probability | Recent form (last 10) |
| ---------- | --------------- | --------------------- |
| Coco Gauff | 90%             | 9-1                   |
| Xinran Sun | 10%             | 7-3                   |

Source: Court Shark model and match data.View data table

[Explore this matchup](https://courtshark.ai/h2h?p1=10221103&p2=12528006)

Court Shark win probabilities are statistical estimates, not advice. No outcome is guaranteed.

Match context

Players

Coco Gauff  
vs Xinran Sun

Event

BEIJING

Surface

Hard

Date

Monday, October 5

Article snapshot · Not live

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Related on Court Shark

- [How our match model works](https://courtshark.ai/blog/methodology/)
- [Open the full head-to-head](https://courtshark.ai/h2h?p1=10221103&p2=12528006)
- [Our editorial standards](https://courtshark.ai/blog/editorial-standards/)
- [About the desk](https://courtshark.ai/blog/about-the-desk/)

## 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.

About the desk

Who writes the Journal and how an article is put together. [Read about the desk](https://courtshark.ai/blog/about-the-desk/)

Editorial standards

What we publish, what we check and how we decide what is fit to print. [Read our standards](https://courtshark.ai/blog/editorial-standards/)

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Spotted an error? Write to hello@courtshark.ai and we will correct it and note the change. [Read the corrections policy](https://courtshark.ai/blog/editorial-standards/)