Here are the three questions I think this dataset answers best. The first is partly answered by the surface breakdown I already pulled, and the other two are what I'd run next. ## 1. Does serve dominance depend on surface, and how large is the effect? The surface aggregates show a big gap in serve output: - **Aces per match:** grass averages about 7.57, carpet 7.94, hard 5.88 and clay 3.38. - **Double faults:** these barely move (2.81 on clay, 2.97 on hard, 3.17 on grass, 3.11 on carpet). So surface changes how many aces a player hits much more than how many double faults. The ace count on clay is less than half that on grass or carpet. There is a sampling caveat. Grass has only 9,479 player-match rows and carpet 2,477, against 117,571 on hard and 107,650 on clay. Carpet in particular is a small, older sample, so I'd treat its numbers with caution. The natural follow-up is to combine `serve_kaggle` (1st%, A%, Df%) with `return_kaggle` (RPW, break-point conversion) by surface. That would show whether a serve advantage on grass or carpet is matched by weaker return pressure there. ## 2. How do serve and return profiles change with the opponent's rank? The data carries the player's own rank (`Rk`) and the opponent's rank (`vRk`), and both the serve and return tables have rank-based gold aggregates. The rank spread by surface already hints at a pattern. Average opponent rank (`vRk`) is higher than the player's own rank (`Rk`) on every surface: | Surface | Average `Rk` | Average `vRk` | |---|---|---| | Hard | 311 | 379 | | Clay | 361 | 434 | | Grass | 151 | 176 | I can't say why from this result alone. One possible reading is that top-ranked players appear more often and face lower-ranked opponents. A fair test of how much a rank gap changes aces, return points won and break-point conversion should control for that. This is the core of a pre-match win-probability or betting model. ## 3. Does form change across rounds, tournaments and years, and do specialists exist? Round (`Rd`, about 14 values), tournament (about 3,963 distinct values in the match tables) and date (2013–2024 in `raw_kaggle`, back to 2006 in `serve_kaggle`) are all available. Combining them with `players_tournament_man_` (player, year, tournament, rounds) would let you: - test whether serve and return stats drop or rise in later rounds, when opponents are stronger; - identify tournament or surface specialists, for example players whose first-serve and return points won are consistently better at one event or surface; - track individual players' trajectories across seasons. ## Caveats - The five tables share no detected join key, so each question would be answered table by table. Linking serve, return and raw stats for the same match would rely on the shared Name, Date, Tournament and Rd columns, which I haven't verified. - `serve_kaggle` starts in 2006, but `raw_kaggle` and `return_kaggle` start in 2013, so serve-versus-return comparisons should be limited to 2013 onward. - The surface aggregate includes 28 rows with a blank surface, which should be excluded.