Here are three questions this dataset can answer well. The first is backed by numbers I already pulled. The other two are framed from the dataset's structure and I have not computed them yet. ## 1. How much does court surface change a player's serve profile, and who benefits most? The surface breakdown of `raw_kaggle` (gold table `raw_kaggle_by_Surface`) shows large differences: | Surface | Matches (player rows) | Avg aces | Avg double faults | Avg serve points (SP) | |---|---|---|---|---| | Hard | 117,571 | 5.88 | 2.97 | 74.2 | | Grass | 9,479 | 7.57 | 3.17 | 84.1 | | Clay | 107,650 | 3.38 | 2.81 | 73.1 | | Carpet | 2,477 | 7.94 | 3.11 | 72.7 | - Grass and carpet produce more than twice the aces of clay, with little change in double faults. - Hard and clay account for nearly all the data. Grass and carpet are thin samples, so they are noisier. - 28 rows have no surface recorded, which is worth excluding. - A natural follow-up is to measure each player's surface-specific deviation from these baselines. That could be used for scouting or as a feature in a match-prediction model. ## 2. Does opponent ranking change how players serve and return? Both `raw_kaggle` and `serve_kaggle` carry the player's own ranking (`Rk`) and the opponent's (`vRk`). `return_kaggle` adds return points won (`RPW`), break-point conversion (`BPCnv`) and return-game stats. - **What to test:** how first-serve points won, ace rate and break-point conversion move as the ranking gap widens. - **Why it matters:** this is the core signal for a pre-match win-probability or betting model. - **Early hint:** average opponent rank differs by surface. On hard courts the average `vRk` is about 379, on clay about 434, and on carpet about 605. The pools of opponents differ by surface, so any surface comparison should control for opponent strength. ## 3. Does a player's form change across rounds, tournaments and years? - **Rounds:** every fact table carries the round (`Rd`, about 14 values) and tournament. You can test whether serve and return performance hold up or decay in later rounds, where opponents are stronger and fatigue builds. - **Years:** `raw_kaggle` and `return_kaggle` start in 2013, while `serve_kaggle` goes back to 2006. `players_tournament_man_` records tournament depth by year from 2002 to 2024. Together these support career-arc analysis, such as peak age and performance against tournament depth. ## Caveats - The five tables share no verified join key, so each question has to be answered per table. Linking a player's serve and return stats in the same match would need a manual match on name, date, tournament and round, which I have not validated. - The data is observational, so it shows association rather than cause.