Francisco Cerundolo: clay giant, hard-court coin flip, and the Khachanov mystery
Francisco Cerundolo has played 237 ATP matches from 2022 to 2025 — the largest match count in the Batch 3 dataset — at a 54.4% overall win rate that is the lowest among active top-20 players in this study. That headline figure understates the analytical interest in his profile: a 60.4% clay win rate, a 7–0 record against Khachanov, a 4–3 record against Ruud on clay, and a 5–2 record against Paul on multiple surfaces. Cerundolo is a classic clay specialist whose value is systematically concentrated on one surface and against specific matchup profiles.
Key metrics at a glance
| Metric | Value |
|---|---|
| Overall win rate | 54.4% |
| Dataset rank (end of period) | 21 |
| Matches analysed | 237 (2022–2025) |
| Best surface | Clay — 60.4% |
| Grand Slam win rate | 47.2% |
| Worst surface | Grass — 38.9% |
| As market favourite | 78.3% |
| As underdog | 36.9% |
Cerundolo's year-by-year record
| Year | Matches | Wins | Win rate |
|---|---|---|---|
| 2022 | 24 | 11 | 45.8% |
| 2023 | 27 | 14 | 51.9% |
| 2024 | 19 | 12 | 63.2% |
| 2025 | 41 | 23 | 56.1% |
Win rate by season, Francisco Cerundolo, 2022–2025. Source: ATP match data via tennispredictor.net
The 2025 figure (56.1% over 41 matches) is notable for its volume — the largest single-season sample in this dataset, reflecting Cerundolo's aggressive scheduling on the ATP tour. His 2024 figure of 63.2% over a smaller 19-match sample was his best season and reflected concentrated clay-season performance. The 2022 figure (45.8%) captures a period when he was establishing himself against top-50 opponents with variable results.
Surface breakdown: clay-dependent profile
| Surface | Matches | Win rate |
|---|---|---|
| Clay | 129 | 60.4% |
| Grand Slam | 36 | 47.2% |
| Hard | 78 | 50.0% |
| Indoors | 11 | 45.5% |
| Grass | 18 | 38.9% |
Win rate by surface, Francisco Cerundolo, 2022–2025. Source: ATP match data via tennispredictor.net
Cerundolo's clay sample (129 matches — the largest clay-surface sample among all 15 players in this study) is the anchor of his profile. The 60.4% clay rate versus 50.0% on hard (78 matches) is a meaningful 10.4-point gap across large samples. On hard courts, he is genuinely a 50% player — every hard-court match against a comparable-ranked opponent is a coin flip.
The Grand Slam figure (47.2%, 36 matches) sits below his overall average. This reflects that Grand Slam draws are typically harder-seeded, and his four Slam hard-court appearances (US Open, AO) weight his hard-court vulnerability into the GS figure.
Grass at 38.9% (18 matches) is consistent with almost no dedicated grass preparation. The market prices him below his ranking on grass, and the data confirms that discount is appropriate.
Round-by-round: the SF collapse
Win rate by round, Francisco Cerundolo, 2022–2025. Source: ATP match data via tennispredictor.net
| Round | Matches | Win rate |
|---|---|---|
| R3 | 5 | 60.0% |
| R2 | 24 | 58.3% |
| R1 | 38 | 57.9% |
| QF | 9 | 55.6% |
| R16 | 27 | 51.9% |
| SF | 7 | 28.6% |
| Final | 1 | 0.0% |
The SF figure of 28.6% (2 wins from 7 appearances) is the most actionable number in his round data. He reaches SFs at a rate higher than expected for his ranking (7 SF appearances reflects regular deep runs), but converts fewer than 1 in 3. This mirrors the same elite ceiling seen across other specialists: when he reaches the SF, he encounters players whose overall level consistently exceeds his clay-specific advantage.
H2H against the elite
H2H win rate vs rivals with 2+ meetings, Francisco Cerundolo, 2022–2025. Source: ATP match data via tennispredictor.net
| Opponent | Record | H2H win rate |
|---|---|---|
| Khachanov | 7–0 | 100.0% |
| Paul | 5–2 | 71.4% |
| Ruud | 4–3 | 57.1% |
| Zverev | 3–1 | 75.0% |
| Rublev | 2–1 | 66.7% |
| Auger-Aliassime | 2–2 | 50.0% |
| Musetti | 1–1 | 50.0% |
| Fritz | 1–1 | 50.0% |
| Sinner | 2–3 | 40.0% |
| Shelton | 0–2 | 0.0% |
| Dimitrov | 0–2 | 0.0% |
| Alcaraz | 0–3 | 0.0% |
The 7–0 record against Khachanov is one of the most extreme positive H2H records in the entire top-20 dataset. Seven matches across multiple events and surfaces, zero losses. Cerundolo's ability to neutralise Khachanov's high-kicking serve with his heavy topspin returns and to maintain patience in extended clay rallies has produced a remarkably one-sided result. When these players draw together, the market consistently prices them near parity — the H2H suggests a strong Cerundolo lean regardless of event size or surface.
The 4–3 record against Ruud is equally interesting. On clay, Cerundolo has proven he can match one of the top-ranked clay specialists in the world — a result that regularly surprises the market and contributes to the positive H2H noted in Ruud's own profile.
The Zverev 3–1 record is also notable, suggesting Cerundolo's clay-court patience specifically disrupts aggressive baseline players.
As market favourite vs underdog
Win rate as market favourite vs underdog, Francisco Cerundolo, 2022–2025. Raw tournament cache. Source: tennispredictor.net
As favourite (120 matches): 78.3% — well above his overall average, confirming strong conversion against lower-ranked opponents. As underdog (149 matches): 36.9% — the largest underdog sample in the Batch 3 dataset, reflecting how often he plays opponents ranked above him. His 36.9% underdog rate is consistent with his 54.4% overall win rate; the market correctly identifies when he is inferior to his opponent.
What the betting market misses about Cerundolo
The Khachanov anomaly is the biggest edge in Batch 3. Seven matches, seven wins. The market prices their matchups based on rankings (both around 20–25); the H2H is as unambiguous as any record in the full top-20 dataset. Back Cerundolo against Khachanov every time.
The clay vs hard split is extreme on a large sample. 60.4% on 129 clay matches vs 50.0% on 78 hard matches — a 10.4-point gap across meaningful samples. At hard-court events, treating Cerundolo as a 50% player against mid-ranked hard specialists is more accurate than any ranking-based pricing.
The Ruud H2H on clay. Cerundolo's 4–3 record against Ruud is the most actionable signal for Roland Garros and other clay Masters draws. When they are in the same quarter, Cerundolo at the underdog price is frequently justified by the H2H data.
How our model treats Cerundolo
- Clay baseline — 60.4% clay rate anchors clay event predictions, applying a positive adjustment over his overall average
- Hard court floor — 50.0% hard rate means the model treats hard-court predictions at near-average probability
- Khachanov positive override — 7–0 H2H is applied as the strongest single positive H2H signal in the dataset
- SF stage discount — 28.6% SF conversion is applied as a strong late-round discount when SF opponents are ranked opponents
Frequently asked questions
What is Cerundolo's overall win rate?
54.4% across 237 matches — the lowest among active top-20 players in this study. But his clay-specific rate (60.4%, 129 matches) is the more relevant figure for clay-event betting.
What is his record against Khachanov?
7–0 across 7 meetings — the most extreme positive H2H record in the Batch 3 dataset, and one of the most extreme in the full 15-player study.
When is Cerundolo worth backing?
Against Khachanov at any stage and on any surface, on clay against higher-ranked hard-court specialists, and in early clay-event rounds at Roland Garros, Monte Carlo, and Barcelona. Fade him on hard courts against hard specialists, in SFs (28.6%), and against Alcaraz at any stage.
What explains the 7-0 record against Khachanov?
Cerundolo's clay-based topspin game absorbs and redirects Khachanov's attacking pace effectively, and his ability to extend rallies neutralises the Russian's preferred quick-strike patterns. The record has held across hard and clay surfaces, suggesting a stylistic matchup rather than a surface-specific phenomenon.
Conclusion
Cerundolo's profile rewards surface-aware and H2H-aware betting more than any other player in the top-20 dataset. His overall 54.4% win rate understates his clay value and the Khachanov edge; his GS figure (47.2%) and grass rate (38.9%) correctly reflect surfaces where he is at a structural disadvantage. The actionable thesis: back him on clay against hard-court specialists, back him against Khachanov universally, and fade him in any late-round scenario on hard or grass.
For the Khachanov profile and the other side of the most one-sided H2H in this study, see the Khachanov analysis.
All statistics sourced from ATP match data 2022–2025. ATP Tour events only. Data extracted October 2025.
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