Badosa vs Kraus Prediction: Guangzhou 2026 Round of 16
Badosa is favored in Guangzhou, but Kraus's stronger return record and recent results support Court Shark's narrower forecast.
Paula Badosa faces Sinja Kraus in Guangzhou's round of 16 with a narrower edge than the market suggests. Court Shark's model gives Badosa a 57% chance, with Kraus's recent form and return record supporting caution.
A quarterfinal place, with different readings of the favorite
The market makes Paula Badosa a clear favorite. Court Shark's model sees Sinja Kraus as a much closer opponent. Both readings favor the same player, but the disagreement concerns how much separation there is between them on a hard court.
They meet in Guangzhou's round of 16 on Wednesday, October 28, with a quarterfinal place available. Badosa brings the higher ranking, while Kraus arrives with the better recent winning record. Each won her opening match here, so the next step requires beating an opponent with a different statistical strength rather than simply extending a favorable comparison.
The ranking gap is modest. Badosa stood at No. 61 and Kraus at No. 76. That supports Badosa's position as favorite, but it is less persuasive evidence for a large advantage than her former place near the top of the rankings might suggest.
Model and market on Paula Badosa
Article snapshot, Oct 27, 2026 · Not live
Paula Badosa's chance as the market consensus priced it from the opening line, next to the Court Shark model.
| Reading | Paula Badosa | Sinja Kraus |
|---|---|---|
| Opening consensus | 71% | 29% |
| Latest consensus | 71% | 29% |
| Court Shark model | 57% | 43% |
Badosa's serve against Kraus's recent results
Badosa's service record offers a concrete reason to favor her. Over the past year on hard courts, she held 67% of her service games, compared with Kraus's 63%. That advantage gives Badosa a plausible route to more comfortable service games, although the return figures point the other way.
Kraus broke in 39% of her return games over that period, while Badosa broke in 35%. The strengths are split. My reading is that this balance supports the model's closer forecast: Badosa's better serving meets an opponent whose return results provide a credible way to challenge it.
Recent results also argue against treating Kraus as a distant underdog: she went 7-3 in her latest matches, compared with Badosa's 6-4. Their recent hard-court records matched those overall records. These are reasons to question the size of the market's preference, not evidence of why the market reached it; prices reveal the assessment, not its explanation.
The rematch and the workload could shift the balance
Badosa won their only previous meeting, on hard courts in Austin, by 7-5 6-1. That is the most direct evidence supporting a more decisive Badosa advantage, but a single meeting cannot establish a dependable pattern. A repeat would require her to turn the service edge into a result against the same opponent again.
Workload could matter. Kraus played five matches in the past fortnight, compared with two for Badosa, although both were listed as fresh. The heavier schedule gives a concrete reason Kraus's recent results might not translate fully into this match; it does not establish that she is fatigued.
A deciding set would add another test, and Badosa's career record offers some support for her if the match reaches that stage under otherwise even conditions. She won 57 of 96 career deciding sets. Kraus won 27 of 49, leaving both with winning records rather than a clear weakness to target.
The prediction
Court Shark's model favors Badosa with a 57% win probability, while the market consensus gives her 71%. The market's preference has remained unchanged since the opening price. The evidence supports favoring Badosa, but Kraus's return results and recent wins make the model's narrower reading more persuasive than a comfortable-favorite interpretation.
Court Shark's AI analyst, which reads the matchup without the market, also picks Badosa and agrees with the model. Her service advantage and previous win provide concrete reasons for that choice. A straight-sets Badosa win is the model's likeliest single outcome, at 38%, and its median match length is 21 games.
The middle half of projected outcomes runs from 18 to 27 games. That is a range, not an exact total. The deciding comparison is Badosa's ability to protect serve against Kraus's stronger return record: the favorite's clearest advantage is tested directly by the underdog's clearest strength.
How long it could go
Article snapshot, Oct 27, 2026 · Not live
A 52% chance the match lasts 18 to 27 games; 21 is the middle of the range.
| Total games | Chance |
|---|---|
| 12 | 0.2% |
| 13 | 1.0% |
| 14 | 2.5% |
| 15 | 4.7% |
| 16 | 7.0% |
| 17 | 8.7% |
| 18 | 8.7% |
| 19 | 8.1% |
| 20 | 7.0% |
| 21 | 5.2% |
| 22 | 6.1% |
| 23 | 3.9% |
| 24 | 2.1% |
| 25 | 3.7% |
| 26 | 3.9% |
| 27 | 3.7% |
| 28 | 3.9% |
| 29 | 4.0% |
| 30 | 3.8% |
| 31 | 3.4% |
| 32 | 2.9% |
| 33 | 1.9% |
| 34 | 1.4% |
| 35 | 1.3% |
| 36 | 0.5% |
| 38 | 0.2% |
Explore this matchup
Compare both players side by side: recent results, surfaces, serve and return.
Player comparison
Article snapshot, Oct 27, 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 | Paula Badosa | Sinja Kraus |
|---|---|---|
| Wins, last 10 matches | 60% (6-4) | 70% (7-3) |
| Wins, last 10 on hard | 60% (6-4) | 70% (7-3) |
| Head-to-head wins | 100% (1-0) | 0% (0-1) |
| Model win probability | 57% | 43% |
Recent form
Article snapshot, Oct 27, 2026 · Not live
Each player's most recent completed matches, up to 10, oldest on the left.
| Player | Date | Result | Opponent | Score | Event |
|---|---|---|---|---|---|
| Paula Badosa | Sep 2, 2026 | W | Daria Kasatkina | 6-1 6-4 | US Open |
| Paula Badosa | Sep 3, 2026 | L | Cori Gauff | 4-6 6-7(5) | US Open |
| Paula Badosa | Sep 16, 2026 | W | Justina Mikulskyte | 6-3 6-2 | Sao Paulo |
| Paula Badosa | Sep 17, 2026 | W | Jazmin Ortenzi | 4-6 6-2 6-2 | Sao Paulo |
| Paula Badosa | Sep 18, 2026 | W | Mary Stoiana | 6-4 6-2 | Sao Paulo |
| Paula Badosa | Sep 19, 2026 | L | Nadia Podoroska | 3-6 4-6 | Sao Paulo |
| Paula Badosa | Oct 1, 2026 | W | Daria Kasatkina | 4-6 7-6(7) 6-4 | Beijing |
| Paula Badosa | Oct 3, 2026 | L | Jelena Ostapenko | 6-7(5) 5-7 | Beijing |
| Paula Badosa | Oct 20, 2026 | L | Antonia Ruzic | 6-3 4-6 3-6 | WTA Osaka |
| Paula Badosa | Oct 26, 2026 | W | Carol Young Suh Lee | 6-1 6-4 | WTA Guangzhou |
| Sinja Kraus | Oct 2, 2026 | W | Anastasia Potapova | 4-1 RET | Beijing |
| Sinja Kraus | Oct 4, 2026 | W | Dayana Yastremska | 6-1 6-4 | Beijing |
| Sinja Kraus | Oct 6, 2026 | L | Nikola Bartunkova | 0-6 6-4 3-6 | Beijing |
| Sinja Kraus | Oct 10, 2026 | W | Magda Linette | 7-6(3) 6-2 | Wuhan |
| Sinja Kraus | Oct 11, 2026 | L | Zeynep Sonmez | 2-6 4-6 | Wuhan |
| Sinja Kraus | Oct 17, 2026 | W | Momoko Kobori | 6-1 3-6 6-2 | WTA Osaka |
| Sinja Kraus | Oct 18, 2026 | W | Sofia Kenin | 7-6(4) 6-4 | WTA Osaka |
| Sinja Kraus | Oct 19, 2026 | W | Caty Mcnally | 6-4 3-1 RET | WTA Osaka |
| Sinja Kraus | Oct 21, 2026 | L | Talia Gibson | 2-6 1-6 | WTA Osaka |
| Sinja Kraus | Oct 26, 2026 | W | Panna Udvardy | 6-4 6-2 | WTA Guangzhou |
Head-to-head by surface
Article snapshot, Oct 27, 2026 · Not live
Paula Badosa and Sinja Kraus have met once. Paula Badosa leads 1-0.
| Surface | Paula Badosa | Sinja Kraus |
|---|---|---|
| All surfaces | 1 | 0 |
| Hard | 1 | 0 |
Win rate by surface
Article snapshot, Oct 27, 2026 · Not live
Share of matches won on each surface across each player's last 50 matches with full statistics.
| Surface | Paula Badosa | Sinja Kraus |
|---|---|---|
| Hard | 50% (13-13) | 60% (12-8) |
| Clay | 68% (13-6) | 74% (17-6) |
| Grass | 40% (2-3) | 43% (3-4) |
Serve and return on hard courts
Article snapshot, Oct 27, 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.
| Measure | Paula Badosa | Sinja Kraus | Tour average, all years |
|---|---|---|---|
| Service games held | 68% | 61% | 63% |
| Return games broken | 35% | 39% | 37% |
| Matches in sample | 26 | 20 |
Model win probability
Article snapshot, Oct 27, 2026 · Not live
The Court Shark model's chance for each player to win this match.
| Player | Win probability | Recent form (last 10) |
|---|---|---|
| Paula Badosa | 57% | 6-4 |
| Sinja Kraus | 43% | 7-3 |
Ways to win
Article snapshot, Oct 27, 2026 · Not live
Every way the match can finish. The likeliest single result is Paula Badosa in straight sets (38%).
| Result | Chance |
|---|---|
| Paula Badosa wins 2-0 | 38% |
| Paula Badosa wins 2-1 | 19% |
| Sinja Kraus wins 2-1 | 17% |
| Sinja Kraus wins 2-0 | 26% |
Service games held: where they stand
Article snapshot, Oct 27, 2026 · Not live
Each dot is one of the WTA top 100 with enough matches in the last 52 weeks. Paula Badosa is 52nd of 64; Sinja Kraus ranks ahead of 1 of 64.
| Player | Service games held | Place |
|---|---|---|
| Paula Badosa | 64.0% | 52nd of 64 |
| Sinja Kraus | 55.5% | Ahead of 1 of 64 |
| Field median | 69.5% | |
| Field best (Elena Rybakina) | 83.0% | 1st |
Two years of form
Article snapshot, Oct 27, 2026 · Not live
Each player's pre-match Elo rating since Dec 28, 2024. Latest: Paula Badosa 1800 (Oct 20, 2026), Sinja Kraus 1764 (Oct 21, 2026).
| Player | Start of window | Two-year peak | Latest |
|---|---|---|---|
| Paula Badosa | 1853 (Jan 1, 2025) | 1863 (Jan 24, 2025) | 1800 (Oct 20, 2026) |
| Sinja Kraus | 1464 (Dec 28, 2024) | 1764 (Oct 21, 2026) | 1764 (Oct 21, 2026) |
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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