| # | Player | W | F | S | Q | R16 | R32 | R64 | R128 | APPS | PTS | PTS/APP |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 201. | Kim Warwick | 0 | 1 | 0 | 3 | 4 | 7 | 10 | 13 | 38 | 4755 | 125 |
| 202. | Roberta Vinci | 0 | 1 | 0 | 3 | 3 | 11 | 6 | 28 | 52 | 4903 | 94 |
| 203. | Zheng Qinwen | 0 | 1 | 0 | 3 | 1 | 3 | 4 | 5 | 17 | 3257 | 192 |
| 204. | Arnaud Clément | 0 | 1 | 0 | 2 | 7 | 5 | 17 | 23 | 55 | 5276 | 96 |
| 205. | Thomas Enqvist | 0 | 1 | 0 | 2 | 6 | 10 | 11 | 16 | 46 | 5198 | 113 |
| 206. | Sharon Walsh | 0 | 1 | 0 | 2 | 6 | 7 | 18 | 19 | 53 | 5304 | 100 |
| 207. | John Sadri | 0 | 1 | 0 | 2 | 3 | 5 | 9 | 8 | 28 | 3822 | 137 |
| 208. | John Lloyd | 0 | 1 | 0 | 2 | 2 | 10 | 13 | 16 | 44 | 4474 | 102 |
| 209. | Flavio Cobolli | 0 | 1 | 0 | 2 | 0 | 4 | 2 | 3 | 12 | 2626 | 219 |
| 210. | Greg Rusedski | 0 | 1 | 0 | 1 | 7 | 7 | 10 | 22 | 48 | 4685 | 98 |
| 211. | Nikola Pilić | 0 | 1 | 0 | 1 | 5 | 7 | 4 | 5 | 23 | 3747 | 163 |
| 212. | MaliVai Washington | 0 | 1 | 0 | 1 | 5 | 3 | 10 | 12 | 32 | 3725 | 116 |
| 213. | Mikael Pernfors | 0 | 1 | 0 | 1 | 3 | 2 | 3 | 11 | 21 | 2779 | 132 |
| 214. | Alberto Berasategui | 0 | 1 | 0 | 1 | 2 | 3 | 3 | 9 | 19 | 2659 | 140 |
| 215. | Leylah Fernandez | 0 | 1 | 0 | 1 | 0 | 4 | 9 | 10 | 25 | 2707 | 108 |
| 216. | Betsy Nagelsen | 0 | 1 | 0 | 0 | 4 | 13 | 15 | 21 | 54 | 4600 | 85 |
| 217. | Chris Lewis | 0 | 1 | 0 | 0 | 2 | 6 | 19 | 7 | 35 | 3502 | 100 |
| 218. | Mal Anderson | 0 | 1 | 0 | 0 | 2 | 4 | 3 | 0 | 10 | 2284 | 228 |
| 219. | Mariano Puerta | 0 | 1 | 0 | 0 | 0 | 1 | 7 | 9 | 18 | 1856 | 103 |
| 220. | Martin Verkerk | 0 | 1 | 0 | 0 | 0 | 1 | 2 | 5 | 9 | 1526 | 170 |
| 221. | John Marks | 0 | 1 | 0 | 0 | 0 | 0 | 6 | 6 | 13 | 1658 | 128 |
| 222. | Maja Chwalińska | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 2 | 4 | 1328 | 332 |
| 223. | Tim Henman | 0 | 0 | 6 | 4 | 6 | 11 | 12 | 10 | 49 | 9346 | 191 |
| 224. | Elina Svitolina | 0 | 0 | 4 | 10 | 8 | 13 | 7 | 8 | 50 | 10546 | 211 |
| 225. | Nikolay Davydenko | 0 | 0 | 4 | 6 | 4 | 5 | 16 | 15 | 50 | 7838 | 157 |
| 226. | Claudia Kohde-Kilsch | 0 | 0 | 4 | 5 | 10 | 8 | 11 | 8 | 46 | 8673 | 189 |
| 227. | Sébastien Grosjean | 0 | 0 | 4 | 5 | 2 | 11 | 10 | 11 | 43 | 7295 | 170 |
| 228. | Tommy Haas | 0 | 0 | 4 | 4 | 8 | 17 | 11 | 16 | 60 | 8928 | 149 |
| 229. | Raúl Ramírez | 0 | 0 | 3 | 6 | 3 | 2 | 8 | 9 | 31 | 6024 | 194 |
| 230. | Grigor Dimitrov | 0 | 0 | 3 | 5 | 11 | 11 | 14 | 17 | 61 | 8727 | 143 |
| 231. | Françoise Dürr | 0 | 0 | 3 | 5 | 11 | 5 | 5 | 2 | 31 | 7395 | 239 |
| 232. | Julie Heldman | 0 | 0 | 3 | 5 | 4 | 6 | 3 | 0 | 21 | 5899 | 281 |
| 233. | Karen Krantzcke | 0 | 0 | 3 | 5 | 3 | 5 | 5 | 0 | 21 | 5695 | 271 |
| 234. | Amanda Coetzer | 0 | 0 | 3 | 4 | 9 | 11 | 16 | 13 | 56 | 7988 | 143 |
| 235. | Kimiko Date | 0 | 0 | 3 | 3 | 4 | 4 | 13 | 24 | 51 | 5709 | 112 |
| 236. | Johanna Konta | 0 | 0 | 3 | 3 | 2 | 0 | 6 | 15 | 29 | 4353 | 150 |
| 237. | Richard Gasquet | 0 | 0 | 3 | 2 | 17 | 15 | 13 | 25 | 75 | 9264 | 124 |
| 238. | John Alexander | 0 | 0 | 3 | 2 | 11 | 6 | 20 | 11 | 53 | 7280 | 137 |
| 239. | Regina Maršíková | 0 | 0 | 3 | 1 | 5 | 4 | 9 | 9 | 31 | 4747 | 153 |
| 240. | Cliff Richey | 0 | 0 | 3 | 1 | 5 | 3 | 5 | 3 | 20 | 4345 | 217 |
| 241. | Roger Taylor | 0 | 0 | 3 | 1 | 3 | 3 | 7 | 11 | 28 | 4121 | 147 |
| 242. | Tom Gorman | 0 | 0 | 3 | 0 | 6 | 5 | 7 | 11 | 32 | 4576 | 143 |
| 243. | Hank Pfister | 0 | 0 | 3 | 0 | 4 | 4 | 8 | 9 | 28 | 4084 | 146 |
| 244. | Manuela Maleeva | 0 | 0 | 2 | 12 | 8 | 10 | 6 | 3 | 41 | 9398 | 229 |
| 245. | Gaël Monfils | 0 | 0 | 2 | 8 | 11 | 18 | 19 | 12 | 70 | 10172 | 145 |
| 246. | Nadia Petrova | 0 | 0 | 2 | 7 | 12 | 14 | 7 | 12 | 54 | 8851 | 164 |
| 247. | Tim Mayotte | 0 | 0 | 2 | 7 | 6 | 4 | 6 | 11 | 36 | 6423 | 178 |
| 248. | Jonas Björkman | 0 | 0 | 2 | 5 | 8 | 10 | 13 | 20 | 58 | 7209 | 124 |
| 249. | Eddie Dibbs | 0 | 0 | 2 | 5 | 1 | 4 | 4 | 2 | 18 | 4377 | 243 |
| 250. | Mikhail Youzhny | 0 | 0 | 2 | 4 | 10 | 14 | 17 | 22 | 69 | 7926 | 115 |
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.