Here are three questions this dataset can answer. The first is backed by a result I ran. The other two are suggested by the table structure and card, and I haven't run them yet. ## 1. Does surface change how a player's serve and return game works, and who benefits most? The surface-level aggregates already show large differences (from the `raw_kaggle_by_Surface` gold table): | Surface | Average aces per match | Average double faults per match | |---|---|---| | Grass | about 7.6 | about 3.2 | | Carpet | about 7.9 | about 3.1 | | Hard | about 5.9 | about 3.0 | | Clay | about 3.4 | about 2.8 | - Match counts are very uneven. There are 117,571 hard-court rows and 107,650 clay rows, against 9,479 on grass and 2,477 on carpet. Grass and carpet averages are therefore noisier. - The average rank of the players also differs by surface: about 151 on grass, 311 on hard, 361 on clay and 517 on carpet. Part of the "surface effect" may really be a difference in who plays there. - The next step would be to compare each player against their own baseline across surfaces, using `raw.raw_kaggle` with `Name`, `Surface`, `Aces`, `DFs` and `1SP`/`2SP`. That would identify surface specialists, such as clay-court players who lose their edge on grass. ## 2. How much does opponent rank shift serve and return performance, and does it differ by round? - **Data:** All three fact tables carry the opponent's rank (`vRk`). Serve data includes `1st%`, `2nd%`, `A%`, `Df%` and break points saved (`Bpsvd`). Return data includes `RPW`, `v1st%`, `v2nd%` and break-point conversion (`BPCnv`). - **What it would show:** - How quickly serve-hold and return-win rates fall off as opponents get stronger. - Whether break-point conversion and saving are skills or mostly noise. - Whether those patterns change in later rounds (`Rd`). - **Existing tables:** The gold tables `return_kaggle_by_Rd_Rk` and `serve_kaggle_by_Rd_Bpsvd` already cover much of this. ## 3. Can pre-match serve and return profiles plus rank predict outcomes, and how does a player's form change across years, tournaments and rounds? - **Why it fits:** The grain is one row per player per match, with `Date`, `Tournament`, `Rd`, `Surface`, own rank and opponent rank (`Rk`, `vRk`). That is enough to build rolling-form features and a win-probability model. - **What to test:** - Whether serve or return stats add predictive power beyond ranking alone. - Whether the model's edge holds on each surface. - Career trajectories, using `players_man_` (`playing_years`, `number_of_matches`) and `players_tournament_man_` (tournament depth by year). - **Caveats:** - The three fact tables have no verified join key, so they would need to be aligned on `Name`, `Date`, `Tournament` and `Rd`. That alignment would have to be validated first. - The serve table covers dates from 2006, while the raw and return tables start in 2013. - 28 rows have no surface recorded.