| # | Player | W | F | S | Q | R16 | R32 | R64 | R128 | APPS | PTS | PTS/APP |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 351. | John Andrews | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 4 | 8 | 715 | 89 |
| 352. | Boro Jovanović | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 5 | 8 | 680 | 85 |
| 353. | Marcelo Filippini | 0 | 0 | 0 | 1 | 1 | 0 | 6 | 12 | 20 | 930 | 47 |
| 354. | Tom Gullikson | 0 | 0 | 0 | 1 | 0 | 9 | 8 | 10 | 28 | 1630 | 58 |
| 355. | Patrick Kuehnen | 0 | 0 | 0 | 1 | 0 | 7 | 9 | 10 | 27 | 1495 | 55 |
| 356. | Bruce Manson | 0 | 0 | 0 | 1 | 0 | 6 | 5 | 11 | 23 | 1235 | 54 |
| 357. | Chris Woodruff | 0 | 0 | 0 | 1 | 0 | 6 | 5 | 8 | 20 | 1205 | 60 |
| 358. | John Millman | 0 | 0 | 0 | 1 | 0 | 5 | 7 | 17 | 30 | 1295 | 43 |
| 359. | Milan Šrejber | 0 | 0 | 0 | 1 | 0 | 5 | 6 | 4 | 16 | 1120 | 70 |
| 360. | Guido Pella | 0 | 0 | 0 | 1 | 0 | 4 | 11 | 16 | 32 | 1375 | 43 |
| 361. | Nicklas Kulti | 0 | 0 | 0 | 1 | 0 | 4 | 10 | 16 | 31 | 1330 | 43 |
| 362. | Shlomo Glickstein | 0 | 0 | 0 | 1 | 0 | 4 | 7 | 13 | 25 | 1165 | 47 |
| 363. | Tomás Martín Etcheverry | 0 | 0 | 0 | 1 | 0 | 4 | 6 | 8 | 19 | 1070 | 56 |
| 364. | Jürgen Fassbender | 0 | 0 | 0 | 1 | 0 | 4 | 5 | 11 | 21 | 1055 | 50 |
| 365. | Ricardo Acuña | 0 | 0 | 0 | 1 | 0 | 4 | 1 | 9 | 15 | 855 | 57 |
| 366. | Peter Fleming | 0 | 0 | 0 | 1 | 0 | 3 | 15 | 8 | 27 | 1385 | 51 |
| 367. | Lu Yen-hsun | 0 | 0 | 0 | 1 | 0 | 3 | 13 | 34 | 51 | 1555 | 30 |
| 368. | Vladimír Zedník | 0 | 0 | 0 | 1 | 0 | 3 | 6 | 10 | 20 | 1000 | 50 |
| 369. | Kelly Evernden | 0 | 0 | 0 | 1 | 0 | 3 | 3 | 14 | 21 | 905 | 43 |
| 370. | Paolo Bertolucci | 0 | 0 | 0 | 1 | 0 | 3 | 2 | 8 | 14 | 800 | 57 |
| 371. | João Fonseca | 0 | 0 | 0 | 1 | 0 | 3 | 2 | 1 | 7 | 730 | 104 |
| 372. | Franco Davín | 0 | 0 | 0 | 1 | 0 | 3 | 0 | 6 | 10 | 690 | 69 |
| 373. | Hendrik Dreekmann | 0 | 0 | 0 | 1 | 0 | 2 | 7 | 13 | 23 | 985 | 43 |
| 374. | Billy Martin | 0 | 0 | 0 | 1 | 0 | 2 | 7 | 13 | 23 | 985 | 43 |
| 375. | Patricio Cornejo | 0 | 0 | 0 | 1 | 0 | 2 | 5 | 18 | 26 | 945 | 36 |
| 376. | Drew Gitlin | 0 | 0 | 0 | 1 | 0 | 2 | 4 | 2 | 9 | 740 | 82 |
| 377. | Paul Chamberlin | 0 | 0 | 0 | 1 | 0 | 2 | 2 | 3 | 8 | 660 | 83 |
| 378. | Alex Rădulescu | 0 | 0 | 0 | 1 | 0 | 2 | 0 | 6 | 9 | 600 | 67 |
| 379. | Lloyd Harris | 0 | 0 | 0 | 1 | 0 | 1 | 10 | 11 | 23 | 1010 | 44 |
| 380. | Peter Feigl | 0 | 0 | 0 | 1 | 0 | 1 | 8 | 5 | 15 | 860 | 57 |
| 381. | Cristiano Caratti | 0 | 0 | 0 | 1 | 0 | 1 | 6 | 14 | 22 | 860 | 39 |
| 382. | Byron Bertram | 0 | 0 | 0 | 1 | 0 | 1 | 5 | 6 | 13 | 735 | 57 |
| 383. | Mark Dickson | 0 | 0 | 0 | 1 | 0 | 1 | 5 | 4 | 11 | 715 | 65 |
| 384. | Rodney Harmon | 0 | 0 | 0 | 1 | 0 | 1 | 2 | 3 | 7 | 570 | 81 |
| 385. | Martin Mulligan | 0 | 0 | 0 | 1 | 0 | 1 | 2 | 1 | 5 | 550 | 110 |
| 386. | Rafael Jódar | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 3 | 495 | 165 |
| 387. | Brad Pearce | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 6 | 8 | 510 | 64 |
| 388. | Shuzo Matsuoka | 0 | 0 | 0 | 1 | 0 | 0 | 9 | 15 | 25 | 915 | 37 |
| 389. | Robin Drysdale | 0 | 0 | 0 | 1 | 0 | 0 | 7 | 7 | 15 | 745 | 50 |
| 390. | André Sá | 0 | 0 | 0 | 1 | 0 | 0 | 5 | 10 | 16 | 685 | 43 |
| 391. | Christopher Eubanks | 0 | 0 | 0 | 1 | 0 | 0 | 4 | 10 | 15 | 640 | 43 |
| 392. | Andreas Seppi | 0 | 0 | 0 | 0 | 6 | 11 | 23 | 27 | 67 | 3375 | 50 |
| 393. | Adrian Mannarino | 0 | 0 | 0 | 0 | 6 | 8 | 18 | 32 | 64 | 2930 | 46 |
| 394. | Paul-Henri Mathieu | 0 | 0 | 0 | 0 | 6 | 8 | 10 | 27 | 51 | 2520 | 49 |
| 395. | Viktor Troicki | 0 | 0 | 0 | 0 | 5 | 10 | 15 | 9 | 39 | 2565 | 66 |
| 396. | Patrice Dominguez | 0 | 0 | 0 | 0 | 5 | 4 | 8 | 8 | 25 | 1700 | 68 |
| 397. | Arnaud Boetsch | 0 | 0 | 0 | 0 | 4 | 11 | 8 | 10 | 33 | 2170 | 66 |
| 398. | Benoît Paire | 0 | 0 | 0 | 0 | 4 | 8 | 16 | 19 | 47 | 2350 | 50 |
| 399. | Francisco Clavet | 0 | 0 | 0 | 0 | 4 | 8 | 12 | 19 | 43 | 2170 | 50 |
| 400. | Christo van Rensburg | 0 | 0 | 0 | 0 | 4 | 7 | 6 | 6 | 23 | 1680 | 73 |
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.