| # | Player | W | F | S | Q | R16 | R32 | R64 | R128 | APPS | PTS | PTS/APP |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1. | Novak Djokovic | 24 | 14 | 17 | 11 | 5 | 7 | 3 | 2 | 83 | 82685 | 996 |
| 2. | Roger Federer | 20 | 11 | 15 | 12 | 11 | 5 | 1 | 6 | 81 | 70855 | 875 |
| 3. | Rafael Nadal | 22 | 8 | 8 | 9 | 7 | 5 | 6 | 3 | 68 | 64610 | 950 |
| 4. | Jimmy Connors | 8 | 7 | 16 | 10 | 2 | 4 | 4 | 7 | 58 | 40490 | 698 |
| 5. | Pete Sampras | 14 | 4 | 5 | 6 | 7 | 2 | 7 | 7 | 52 | 40385 | 777 |
| 6. | Ivan Lendl | 8 | 11 | 9 | 6 | 8 | 4 | 4 | 7 | 57 | 39890 | 700 |
| 7. | Andre Agassi | 8 | 7 | 11 | 10 | 6 | 3 | 7 | 9 | 61 | 37675 | 618 |
| 8. | Björn Borg | 11 | 5 | 1 | 4 | 4 | 1 | 1 | 0 | 27 | 31015 | 1149 |
| 9. | John McEnroe | 7 | 4 | 8 | 7 | 7 | 2 | 4 | 6 | 45 | 28760 | 639 |
| 10. | Stefan Edberg | 6 | 5 | 8 | 7 | 7 | 4 | 13 | 4 | 54 | 28525 | 528 |
| 11. | Andy Murray | 3 | 8 | 10 | 9 | 8 | 7 | 7 | 8 | 60 | 28505 | 475 |
| 12. | Boris Becker | 6 | 4 | 8 | 5 | 8 | 5 | 5 | 5 | 46 | 26525 | 577 |
| 13. | Mats Wilander | 7 | 4 | 3 | 6 | 6 | 5 | 8 | 5 | 44 | 25060 | 570 |
| 14. | Guillermo Vilas | 4 | 4 | 4 | 7 | 8 | 7 | 6 | 9 | 49 | 20630 | 421 |
| 15. | Carlos Alcaraz | 7 | 1 | 2 | 4 | 1 | 2 | 3 | 0 | 20 | 18575 | 929 |
| 16. | John Newcombe | 5 | 2 | 3 | 8 | 3 | 2 | 0 | 3 | 26 | 18190 | 700 |
| 17. | Ken Rosewall | 4 | 4 | 5 | 2 | 4 | 3 | 1 | 0 | 23 | 18155 | 789 |
| 18. | Stan Wawrinka | 3 | 1 | 5 | 9 | 10 | 15 | 14 | 20 | 77 | 18020 | 234 |
| 19. | Jim Courier | 4 | 3 | 4 | 4 | 7 | 4 | 8 | 8 | 42 | 17980 | 428 |
| 20. | Jannik Sinner | 5 | 2 | 3 | 5 | 5 | 0 | 3 | 4 | 27 | 17435 | 646 |
| 21. | Alexander Zverev | 1 | 4 | 7 | 6 | 7 | 8 | 4 | 5 | 42 | 16210 | 386 |
| 22. | Lleyton Hewitt | 2 | 2 | 4 | 7 | 15 | 10 | 9 | 17 | 66 | 15975 | 242 |
| 23. | Arthur Ashe | 3 | 2 | 5 | 5 | 9 | 4 | 1 | 2 | 31 | 15845 | 511 |
| 24. | Andy Roddick | 1 | 4 | 5 | 9 | 5 | 9 | 6 | 7 | 46 | 15690 | 341 |
| 25. | Marin Čilić | 1 | 2 | 3 | 8 | 14 | 13 | 9 | 15 | 65 | 13685 | 211 |
| 26. | Michael Chang | 1 | 3 | 4 | 5 | 7 | 9 | 16 | 12 | 57 | 13190 | 231 |
| 27. | Tomáš Berdych | 0 | 1 | 6 | 10 | 16 | 7 | 8 | 13 | 61 | 13120 | 215 |
| 28. | Daniil Medvedev | 1 | 5 | 3 | 2 | 7 | 5 | 3 | 11 | 37 | 12835 | 347 |
| 29. | Rod Laver | 5 | 1 | 0 | 1 | 6 | 1 | 1 | 0 | 15 | 12775 | 852 |
| 30. | Ilie Năstase | 2 | 3 | 1 | 6 | 5 | 7 | 10 | 9 | 43 | 12550 | 292 |
| 31. | Goran Ivanišević | 1 | 3 | 3 | 7 | 5 | 9 | 9 | 14 | 51 | 12535 | 246 |
| 32. | David Ferrer | 0 | 1 | 5 | 11 | 10 | 15 | 11 | 10 | 63 | 12505 | 198 |
| 33. | Jan Kodeš | 3 | 2 | 1 | 4 | 6 | 3 | 8 | 9 | 36 | 12360 | 343 |
| 34. | Marat Safin | 2 | 2 | 3 | 2 | 7 | 7 | 11 | 7 | 41 | 11735 | 286 |
| 35. | Stan Smith | 2 | 1 | 2 | 5 | 13 | 6 | 7 | 8 | 44 | 11715 | 266 |
| 36. | Yevgeny Kafelnikov | 2 | 1 | 3 | 7 | 3 | 8 | 11 | 3 | 38 | 11665 | 307 |
| 37. | Jo-Wilfried Tsonga | 0 | 1 | 5 | 9 | 10 | 10 | 5 | 8 | 48 | 11045 | 230 |
| 38. | Vitas Gerulaitis | 1 | 2 | 5 | 3 | 6 | 3 | 9 | 9 | 38 | 10925 | 288 |
| 39. | Pat Rafter | 2 | 2 | 3 | 0 | 6 | 6 | 5 | 11 | 35 | 10515 | 300 |
| 40. | Juan Martín del Potro | 1 | 1 | 4 | 7 | 4 | 7 | 10 | 3 | 37 | 10430 | 282 |
| 41. | Juan Carlos Ferrero | 1 | 2 | 3 | 3 | 6 | 13 | 9 | 9 | 46 | 10385 | 226 |
| 42. | Johan Kriek | 2 | 0 | 3 | 6 | 3 | 11 | 5 | 6 | 36 | 10135 | 282 |
| 43. | Roscoe Tanner | 1 | 1 | 4 | 5 | 7 | 8 | 4 | 4 | 34 | 10080 | 296 |
| 44. | Tony Roche | 0 | 3 | 5 | 4 | 5 | 4 | 2 | 2 | 25 | 10010 | 400 |
| 45. | Dominic Thiem | 1 | 3 | 2 | 2 | 7 | 2 | 7 | 13 | 37 | 9645 | 261 |
| 46. | Todd Martin | 0 | 2 | 4 | 4 | 7 | 11 | 11 | 9 | 48 | 9555 | 199 |
| 47. | Michael Stich | 1 | 2 | 3 | 4 | 4 | 5 | 7 | 6 | 32 | 9545 | 298 |
| 48. | Gustavo Kuerten | 3 | 0 | 0 | 5 | 3 | 3 | 9 | 10 | 33 | 9115 | 276 |
| 49. | Gaël Monfils | 0 | 0 | 2 | 8 | 11 | 18 | 19 | 12 | 70 | 8895 | 127 |
| 50. | Pat Cash | 1 | 2 | 2 | 4 | 5 | 3 | 6 | 11 | 34 | 8830 | 260 |
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.