Three questions stand out, because the dataset has the serve, return, rank and surface fields they need. I've listed the caveats that apply to each. ## 1. Does surface change a player's serve profile once rank is accounted for? The surface gold table (`raw_kaggle_by_Surface`) shows large differences in raw averages: | Surface | Matches | Avg aces | Avg double faults | Avg player rank (`Rk`) | |---|---|---|---|---| | Hard | 117,571 | 5.88 | 2.97 | 310.8 | | Grass | 9,479 | 7.57 | 3.17 | 151.2 | | Clay | 107,650 | 3.38 | 2.81 | 361.4 | | Carpet | 2,477 | 7.94 | 3.11 | 517.4 | Aces are more than twice as frequent on grass and carpet as on clay. The raw averages are confounded, though. Grass players average a rank of about 151 against about 361 on clay, which suggests stronger players are over-represented on grass. The real question is whether the surface effect on aces, first-serve percentage and points won on serve holds after controlling for `Rk` and opponent rank (`vRk`). That would also show which players over- or under-perform on each surface for scouting. ## 2. How much does the ranking gap (`Rk` vs `vRk`) explain about serve and return performance and match outcomes? Each match has the player's rank, the opponent's rank and the point-level stats. They are the raw ingredients of a pre-match win-probability or betting model. The analysis would: - Measure how strongly the rank gap predicts return points won (`RPW`) and total points won (`TPW`). - Find where the rank gap stops mattering, for example on high-variance grass or carpet where big servers may narrow it. - Identify matches where results beat what rank would predict, which points to upsets and mispriced players. ## 3. Does form change across rounds, tournaments and time, and who performs better under pressure? The data has 14 distinct rounds (`Rd`), about 3,963 tournament values and dates from 2006/2013 to September 2024. It also has break-point conversion (`BPCnv`) and break points saved (`Bpsvd`). This makes it possible to test: - Whether serve and return quality change from early rounds to finals. - Whether break-point conversion and saving separate elite players from the rest, independent of baseline serve and return strength. - Which tournaments or surfaces produce consistently stronger or weaker performance. ## Caveats - The five tables share no detected join keys. Serve, return and raw stats would have to be matched by name, date, tournament and round, and that match is unverified. - `serve_kaggle` starts in 2006, while `raw_kaggle` and `return_kaggle` start in 2013, so time-based comparisons have uneven coverage. - 28 rows in the surface table have no surface label. - I have not checked whether a win/loss column exists. If it doesn't, outcomes would have to be inferred from points won.