# Three analytical questions this dataset could answer I got a schema overview and one surface-level summary. The later steps repeated the same result or failed to parse, so I never reached the round, tournament, or break-point breakdowns. The surface figures below are real. The questions about rounds and break points are proposals that I haven't tested. ## What the data contains - There are 37 gold tables, all pre-aggregated (sum, avg, min, max, median): - `raw_kaggle` (6 tables) covers 9 metrics by round, surface, and tournament. - `return_kaggle` (21 tables) covers vRk by round, rank, surface, tournament, BPCnv, and "against". - `serve_kaggle` (10 tables) covers Rk and vRk by round, surface, tournament, and Bpsvd. - The surface summary covers about 237,205 rows, which I summed from the per-surface counts: | Surface | Rows | Avg aces | Avg DFs | Avg Rk | Avg vRk | Avg TP | |---|---|---|---|---|---|---| | Hard | 117,571 | 5.88 | 2.97 | 310.8 | 378.6 | 148.9 | | Clay | 107,650 | 3.38 | 2.81 | 361.4 | 433.7 | 146.7 | | Grass | 9,479 | 7.57 | 3.17 | 151.2 | 176.2 | 168.4 | | Carpet | 2,477 | 7.94 | 3.11 | 517.4 | 604.7 | 146.0 | | (blank) | 28 | – | – | 550.1 | 695.0 | – | The data dictionary wasn't available, so I'm reading Rk as the player's rank, vRk as the opponent's rank, vA as the opponent's aces, and TP as total points. Treat these readings as assumptions. ## The three questions ### 1. How much does surface drive serve dominance, and does it hold after controlling for player quality? - **Supported by the data:** Average aces are much higher on grass (7.57) and carpet (7.94) than on hard (5.88) and clay (3.38). Opponent aces (vA) follow the same pattern: 7.59, 8.13, 5.74, and 3.21. - **Why it isn't settled:** Average rank also differs widely by surface. Grass averages 151 against 361 on clay, so the two surfaces have very different player pools. The gap in aces could reflect who plays on each surface rather than the surface itself. - **Next step:** Compare aces, first-serve points, and double faults by surface within rank bands, using the tables that carry an Rk dimension. ### 2. Is there a systematic rank asymmetry between players and their opponents, and what does it say about how the data is structured? - **Supported by the data:** On every surface, average vRk is higher than average Rk, meaning a worse-ranked opponent. The gaps are 378.6 vs 310.8 on hard, 433.7 vs 361.4 on clay, 176.2 vs 151.2 on grass, and 604.7 vs 517.4 on carpet. - **Uncertain:** This may mean the focal player is more often the higher-ranked or winning side. It could also come from how rows were built, or from rank coverage differing between players and opponents. I haven't checked which. - **Why it matters:** If the rows are biased toward winners, any raw serve or return average is biased too. The question is worth answering before trusting the other analyses. ### 3. Does pressure performance (break points saved and converted) depend on rank, round, and surface? - **Supported by the data:** The schema includes `BPCnv` (break points converted) on the return side and `Bpsvd` (break points saved) on the serve side. Both can be broken down by round, surface, tournament, and rank. - **Not tested:** I never retrieved values for these tables. I can't say whether top-ranked players convert or save break points at higher rates, or whether this changes in later rounds. - **Next step:** Query the BPCnv and Bpsvd tables by round and rank band, and compare conversion against the rank gap (Rk vs vRk). ## Caveats - The data is pre-aggregated, so match-level modeling such as win probability isn't possible from these tables alone. - I haven't seen any date or year field, so I can't say whether trends over time are answerable. - The 28 rows with a blank surface should be excluded or investigated before any surface comparison.