| # | Player | W | F | S | Q | R16 | R32 | R64 | R128 | APPS | PTS | PTS/APP |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1. | Novak Djokovic | 24 | 14 | 17 | 11 | 5 | 7 | 3 | 3 | 84 | 82695 | 984 |
| 2. | Rafael Nadal | 22 | 8 | 8 | 9 | 7 | 5 | 6 | 3 | 68 | 64610 | 950 |
| 3. | Roger Federer | 20 | 11 | 15 | 12 | 11 | 5 | 1 | 6 | 81 | 70855 | 875 |
| 4. | Pete Sampras | 14 | 4 | 5 | 6 | 7 | 2 | 7 | 7 | 52 | 40385 | 777 |
| 5. | Björn Borg | 11 | 5 | 1 | 4 | 4 | 1 | 1 | 0 | 27 | 31015 | 1149 |
| 6. | Ivan Lendl | 8 | 11 | 9 | 6 | 8 | 4 | 4 | 7 | 57 | 39890 | 700 |
| 7. | Jimmy Connors | 8 | 7 | 16 | 10 | 2 | 4 | 4 | 7 | 58 | 40490 | 698 |
| 8. | Andre Agassi | 8 | 7 | 11 | 10 | 6 | 3 | 7 | 9 | 61 | 37675 | 618 |
| 9. | John McEnroe | 7 | 4 | 8 | 7 | 7 | 2 | 4 | 6 | 45 | 28760 | 639 |
| 10. | Mats Wilander | 7 | 4 | 3 | 6 | 6 | 5 | 8 | 5 | 44 | 25060 | 570 |
| 11. | Carlos Alcaraz | 7 | 1 | 2 | 5 | 1 | 2 | 3 | 0 | 21 | 18935 | 902 |
| 12. | Stefan Edberg | 6 | 5 | 8 | 7 | 7 | 4 | 13 | 4 | 54 | 28525 | 528 |
| 13. | Boris Becker | 6 | 4 | 8 | 5 | 8 | 5 | 5 | 5 | 46 | 26525 | 577 |
| 14. | John Newcombe | 5 | 2 | 3 | 8 | 3 | 2 | 0 | 3 | 26 | 18190 | 700 |
| 15. | Jannik Sinner | 5 | 2 | 3 | 5 | 5 | 0 | 3 | 4 | 27 | 17435 | 646 |
| 16. | Rod Laver | 5 | 1 | 0 | 1 | 6 | 1 | 1 | 0 | 15 | 12775 | 852 |
| 17. | Ken Rosewall | 4 | 4 | 5 | 2 | 4 | 3 | 1 | 0 | 23 | 18155 | 789 |
| 18. | Guillermo Vilas | 4 | 4 | 4 | 7 | 8 | 7 | 6 | 9 | 49 | 20630 | 421 |
| 19. | Jim Courier | 4 | 3 | 4 | 4 | 7 | 4 | 8 | 8 | 42 | 17980 | 428 |
| 20. | Andy Murray | 3 | 8 | 10 | 9 | 8 | 7 | 7 | 8 | 60 | 28505 | 475 |
| 21. | Arthur Ashe | 3 | 2 | 5 | 5 | 9 | 4 | 1 | 2 | 31 | 15845 | 511 |
| 22. | Jan Kodeš | 3 | 2 | 1 | 4 | 6 | 3 | 8 | 9 | 36 | 12360 | 343 |
| 23. | Stan Wawrinka | 3 | 1 | 5 | 9 | 10 | 15 | 14 | 21 | 78 | 18030 | 231 |
| 24. | Gustavo Kuerten | 3 | 0 | 0 | 5 | 3 | 3 | 9 | 10 | 33 | 9115 | 276 |
| 25. | Alexander Zverev | 2 | 4 | 7 | 6 | 7 | 8 | 4 | 5 | 43 | 18210 | 423 |
| 26. | Ilie Năstase | 2 | 3 | 1 | 6 | 5 | 7 | 10 | 9 | 43 | 12550 | 292 |
| 27. | Lleyton Hewitt | 2 | 2 | 4 | 7 | 15 | 10 | 9 | 17 | 66 | 15975 | 242 |
| 28. | Marat Safin | 2 | 2 | 3 | 2 | 7 | 7 | 11 | 7 | 41 | 11735 | 286 |
| 29. | Pat Rafter | 2 | 2 | 3 | 0 | 6 | 6 | 5 | 11 | 35 | 10515 | 300 |
| 30. | Yevgeny Kafelnikov | 2 | 1 | 3 | 7 | 3 | 8 | 11 | 3 | 38 | 11665 | 307 |
| 31. | Stan Smith | 2 | 1 | 2 | 5 | 13 | 6 | 7 | 8 | 44 | 11715 | 266 |
| 32. | Sergi Bruguera | 2 | 1 | 1 | 0 | 5 | 2 | 11 | 11 | 33 | 7605 | 230 |
| 33. | Johan Kriek | 2 | 0 | 3 | 6 | 3 | 11 | 5 | 6 | 36 | 10135 | 282 |
| 34. | Daniil Medvedev | 1 | 5 | 3 | 2 | 8 | 5 | 3 | 11 | 38 | 13015 | 343 |
| 35. | Andy Roddick | 1 | 4 | 5 | 9 | 5 | 9 | 6 | 7 | 46 | 15690 | 341 |
| 36. | Michael Chang | 1 | 3 | 4 | 5 | 7 | 9 | 16 | 12 | 57 | 13190 | 231 |
| 37. | Goran Ivanišević | 1 | 3 | 3 | 7 | 5 | 9 | 9 | 14 | 51 | 12535 | 246 |
| 38. | Dominic Thiem | 1 | 3 | 2 | 2 | 7 | 2 | 7 | 13 | 37 | 9645 | 261 |
| 39. | Vitas Gerulaitis | 1 | 2 | 5 | 3 | 6 | 3 | 9 | 9 | 38 | 10925 | 288 |
| 40. | Marin Čilić | 1 | 2 | 3 | 8 | 14 | 13 | 9 | 15 | 65 | 13685 | 211 |
| 41. | Michael Stich | 1 | 2 | 3 | 4 | 4 | 5 | 7 | 6 | 32 | 9545 | 298 |
| 42. | Juan Carlos Ferrero | 1 | 2 | 3 | 3 | 6 | 13 | 9 | 9 | 46 | 10385 | 226 |
| 43. | Pat Cash | 1 | 2 | 2 | 4 | 5 | 3 | 6 | 11 | 34 | 8830 | 260 |
| 44. | Juan Martín del Potro | 1 | 1 | 4 | 7 | 4 | 7 | 10 | 3 | 37 | 10430 | 282 |
| 45. | Roscoe Tanner | 1 | 1 | 4 | 5 | 7 | 8 | 4 | 4 | 34 | 10080 | 296 |
| 46. | Manuel Orantes | 1 | 1 | 2 | 5 | 4 | 4 | 5 | 8 | 30 | 7825 | 261 |
| 47. | Andrés Gimeno | 1 | 1 | 2 | 1 | 3 | 2 | 2 | 3 | 15 | 5840 | 389 |
| 48. | Carlos Moyá | 1 | 1 | 1 | 5 | 5 | 5 | 15 | 14 | 47 | 7885 | 168 |
| 49. | Petr Korda | 1 | 1 | 0 | 4 | 4 | 5 | 10 | 14 | 39 | 6400 | 164 |
| 50. | Richard Krajicek | 1 | 0 | 3 | 5 | 5 | 12 | 9 | 5 | 40 | 8395 | 210 |
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.