| # | Player | W | F | S | Q | R16 | R32 | R64 | R128 | APPS | PTS | PTS/APP |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 601. | Ion Țiriac | 0 | 0 | 0 | 1 | 2 | 3 | 10 | 4 | 20 | 1765 | 88 |
| 602. | Brian Fairlie | 0 | 0 | 0 | 1 | 2 | 3 | 9 | 5 | 20 | 1717 | 86 |
| 603. | Cristian Garín | 0 | 0 | 0 | 1 | 2 | 3 | 8 | 9 | 23 | 1699 | 74 |
| 604. | Brett Steven | 0 | 0 | 0 | 1 | 2 | 3 | 8 | 9 | 23 | 1699 | 74 |
| 605. | Shinobu Asagoe | 0 | 0 | 0 | 1 | 2 | 3 | 6 | 13 | 25 | 1623 | 65 |
| 606. | Elena Bovina | 0 | 0 | 0 | 1 | 2 | 3 | 6 | 7 | 19 | 1563 | 82 |
| 607. | Kathleen Harter | 0 | 0 | 0 | 1 | 2 | 3 | 5 | 5 | 16 | 1485 | 93 |
| 608. | Ronald Agénor | 0 | 0 | 0 | 1 | 2 | 2 | 8 | 16 | 29 | 1659 | 57 |
| 609. | Sybille Bammer | 0 | 0 | 0 | 1 | 2 | 2 | 8 | 11 | 24 | 1609 | 67 |
| 610. | Mariano Zabaleta | 0 | 0 | 0 | 1 | 2 | 2 | 6 | 19 | 30 | 1573 | 52 |
| 611. | Lucia Romanov-Stark | 0 | 0 | 0 | 1 | 2 | 2 | 4 | 6 | 15 | 1327 | 88 |
| 612. | Dája Bedáňová | 0 | 0 | 0 | 1 | 2 | 2 | 3 | 6 | 14 | 1269 | 91 |
| 613. | Naoko Sato | 0 | 0 | 0 | 1 | 2 | 2 | 3 | 3 | 11 | 1239 | 113 |
| 614. | Patricia Coleman | 0 | 0 | 0 | 1 | 2 | 2 | 3 | 0 | 8 | 1209 | 151 |
| 615. | Ágnes Szávay | 0 | 0 | 0 | 1 | 2 | 1 | 6 | 7 | 17 | 1343 | 79 |
| 616. | Iva Jovic | 0 | 0 | 0 | 1 | 2 | 1 | 4 | 1 | 9 | 1167 | 130 |
| 617. | Alberto Mancini | 0 | 0 | 0 | 1 | 2 | 1 | 2 | 6 | 12 | 1101 | 92 |
| 618. | Learner Tien | 0 | 0 | 0 | 1 | 2 | 1 | 2 | 5 | 11 | 1091 | 99 |
| 619. | Guillermo Pérez Roldán | 0 | 0 | 0 | 1 | 2 | 1 | 0 | 6 | 10 | 985 | 99 |
| 620. | Jan Lehane | 0 | 0 | 0 | 1 | 2 | 1 | 0 | 0 | 4 | 925 | 231 |
| 621. | Ctislav Doseděl | 0 | 0 | 0 | 1 | 2 | 0 | 16 | 11 | 30 | 1853 | 62 |
| 622. | Odile de Roubin | 0 | 0 | 0 | 1 | 2 | 0 | 6 | 6 | 15 | 1223 | 82 |
| 623. | Kathy Kuykendall | 0 | 0 | 0 | 1 | 2 | 0 | 3 | 0 | 6 | 989 | 165 |
| 624. | István Gulyás | 0 | 0 | 0 | 1 | 2 | 0 | 2 | 7 | 12 | 1001 | 83 |
| 625. | Cynthia Doerner | 0 | 0 | 0 | 1 | 1 | 9 | 9 | 3 | 23 | 2147 | 93 |
| 626. | Kimberly Po | 0 | 0 | 0 | 1 | 1 | 9 | 7 | 11 | 29 | 2111 | 73 |
| 627. | Cecilia Martinez | 0 | 0 | 0 | 1 | 1 | 9 | 5 | 1 | 17 | 1895 | 111 |
| 628. | Andrew Pattison | 0 | 0 | 0 | 1 | 1 | 8 | 11 | 10 | 31 | 2223 | 72 |
| 629. | Alicia Molik | 0 | 0 | 0 | 1 | 1 | 8 | 10 | 19 | 39 | 2255 | 58 |
| 630. | Terry Phelps | 0 | 0 | 0 | 1 | 1 | 7 | 7 | 11 | 27 | 1891 | 70 |
| 631. | Victor Hănescu | 0 | 0 | 0 | 1 | 1 | 6 | 12 | 19 | 39 | 2151 | 55 |
| 632. | Stefan Koubek | 0 | 0 | 0 | 1 | 1 | 6 | 12 | 18 | 38 | 2141 | 56 |
| 633. | Inés Gorrochategui | 0 | 0 | 0 | 1 | 1 | 6 | 8 | 8 | 24 | 1809 | 75 |
| 634. | Bill Bowrey | 0 | 0 | 0 | 1 | 1 | 6 | 5 | 1 | 14 | 1565 | 112 |
| 635. | Sandra Cecchini | 0 | 0 | 0 | 1 | 1 | 5 | 11 | 18 | 36 | 1973 | 55 |
| 636. | Scott Davis | 0 | 0 | 0 | 1 | 1 | 5 | 11 | 16 | 34 | 1953 | 57 |
| 637. | Marie Neumanova | 0 | 0 | 0 | 1 | 1 | 5 | 11 | 12 | 30 | 1913 | 64 |
| 638. | Wang Qiang | 0 | 0 | 0 | 1 | 1 | 5 | 9 | 13 | 29 | 1807 | 62 |
| 639. | Renzo Furlan | 0 | 0 | 0 | 1 | 1 | 5 | 4 | 16 | 27 | 1547 | 57 |
| 640. | Denisa Chládková | 0 | 0 | 0 | 1 | 1 | 4 | 11 | 18 | 35 | 1863 | 53 |
| 641. | Albert Ramos Viñolas | 0 | 0 | 0 | 1 | 1 | 4 | 10 | 35 | 51 | 1975 | 39 |
| 642. | Frew McMillan | 0 | 0 | 0 | 1 | 1 | 4 | 8 | 12 | 26 | 1629 | 63 |
| 643. | Adrian Voinea | 0 | 0 | 0 | 1 | 1 | 4 | 7 | 14 | 27 | 1591 | 59 |
| 644. | Mikael Tillström | 0 | 0 | 0 | 1 | 1 | 4 | 6 | 9 | 21 | 1483 | 71 |
| 645. | Tomás Martín Etcheverry | 0 | 0 | 0 | 1 | 1 | 4 | 6 | 8 | 20 | 1473 | 74 |
| 646. | Jan-Michael Gambill | 0 | 0 | 0 | 1 | 1 | 3 | 12 | 14 | 31 | 1771 | 57 |
| 647. | Anna-Lena Grönefeld | 0 | 0 | 0 | 1 | 1 | 3 | 4 | 12 | 21 | 1287 | 61 |
| 648. | Heidi Eisterlehner | 0 | 0 | 0 | 1 | 1 | 3 | 4 | 7 | 16 | 1237 | 77 |
| 649. | Joyce Barclay | 0 | 0 | 0 | 1 | 1 | 3 | 4 | 2 | 11 | 1187 | 108 |
| 650. | Mischa Zverev | 0 | 0 | 0 | 1 | 1 | 3 | 3 | 20 | 28 | 1309 | 47 |
Open Era Grand Slam Tennis
This site is dedicated to Grand Slam tennis stats from the Open Era – for those of us who love the numbers behind the game. Pick any group of players and see how they rank. Compare, filter, and view the same data from different angles – whether you want to explore dominance, consistency, or longevity. In short: It’s all about turning tennis history into insights you can actually play with.