## Three questions this dataset can answer These come from the dataset's structure (per-match serve, return and raw stats for each player, with surface, round, tournament, date and opponent rank). The surface numbers below are the only ones I actually queried. The other two questions are proposed, and I have not run them yet. ### 1. How much does surface change a player's serve profile, and who is a surface specialist? The surface breakdown already shows large differences in serve outcomes: | Surface | Avg aces per match | Avg first-serve points won (`1SP`) | |---|---|---| | Carpet | 7.94 | 43.9 | | Grass | 7.57 | 53.0 | | Hard | 5.88 | 45.3 | | Clay | 3.38 | 45.0 | - Aces on grass and carpet are more than double clay. Double faults barely move (about 2.8 to 3.2 per match), so the surface effect comes mainly from aces and first-serve points won, not from errors. - Grass players win about 8 more first-serve points per match than players on hard or clay. - The natural next step is to compute each player's deviation from these surface baselines, to find specialists. That is useful for scouting and match prediction. - Carpet (2,477 rows) and grass (9,479 rows) are far smaller than hard (117,571) and clay (107,650), so player-level surface estimates will be noisy on those two. ### 2. How do serve and return performance change with opponent rank? Every match row carries the player's rank (`Rk`) and the opponent's rank (`vRk`). The return table also has return-points-won fields and break-point conversion. That lets you ask: - How quickly does serve and return effectiveness drop as the opponent's rank improves? - Is the drop steeper on some surfaces? - Do high-ranked players gain more from their serve or from their return? Rank is not evenly spread across surfaces. Average player rank is about 151 on grass, 311 on hard, 361 on clay and 517 on carpet. Grass events seem to draw a stronger field, so any surface comparison has to control for rank or it will mistake field quality for a surface effect. ### 3. Does form and pressure performance change across rounds, tournaments and years? The data has about 14 round levels, roughly 3,963 tournaments and dates from 2006 to 2024. It supports questions like: - Do players serve or return differently in finals than in early rounds? - Does break-point conversion or saving differ from their overall level, a measure of clutch play? - Do career trajectories show up in the stats year by year? The pre-aggregated round, tournament and break-point tables make this cheap to compute. A sensible extension is to use these features (surface-adjusted serve and return, rank gap, round) as inputs to a pre-match win-probability model. ### Caveats - The three fact tables have no verified join key, so each question has to be answered within a single table, or matched manually on player, date, tournament and round. - The data is observational. Rank and surface differences are confounded, as noted above.