# Three analytical questions this dataset can answer These come from the dataset's structure (one row per player per match, with serve, return and ranking stats) and from the surface breakdown I already pulled from `raw_kaggle_by_Surface`. ## 1. How much does surface change a player's serve profile, and which players are surface specialists? The surface split is large and shows up in the serve numbers: | Surface | Matches (player-rows) | Avg aces per match | Avg double faults per match | |---|---|---|---| | Grass | 9,479 | about 7.6 | about 3.2 | | Carpet | 2,477 | about 7.9 | about 3.1 | | Hard | 117,571 | about 5.9 | about 3.0 | | Clay | 107,650 | about 3.4 | about 2.8 | - Grass and carpet produce roughly twice the aces of clay. - Grass has the highest average serve points (`SP`, about 84, against about 73 on clay). - Average `Rk` is about 151 on grass and about 361 on clay. That suggests top-ranked players are over-represented on grass. - Because the data is per player and per match, the next step is to rank players by their surface-specific serve and return points won. That would show who over- or under-performs relative to their overall level, which is useful for scouting and match prediction. ## 2. Does ranking gap predict serve and return dominance, and where does that edge disappear? - Each row carries the player's rank (`Rk`) and the opponent's rank (`vRk`). - Return-side tables add return points won (`RPW`), break-point conversion (`BPCnv`) and serve-side break points saved (`Bpsvd`). - You could bucket matches by rank gap (for example, top 10 against ranks 100+) and measure how aces, first-serve points won and break-point conversion change. - Adding round (`Rd`) and surface would show whether upsets are more likely on certain surfaces or in early rounds. - The gold tables `return_kaggle_by_Rk` and `return_kaggle_by_Surface_Rk` are already aggregated for this. - This is the core input for a pre-match win-probability or betting model. ## 3. Does performance or playing style change across rounds, tournaments and years? - The data spans 2006 or 2013 to 2024 (serve data starts in 2006, return and raw in 2013), with about 14 distinct rounds (`Rd`) and about 3,963 distinct tournament labels. - You could test whether players serve better or worse in later rounds, and whether "big-server" profiles have become more common over time. - Tournament depth from `players_tournament_man_` adds a second angle: which events or players reach the later rounds most often. - The gold tables `*_by_Rd`, `*_by_Tournament` and `*_by_Tournament_Rd` support this directly. ## Caveats - Only 28 rows have no `Surface` label (out of 237,205). They are too few to matter, but filter them out. - The five tables share no verified join key. Serve, return and raw stats therefore have to be analysed per table, not combined row by row. Matching them on Name + Date + Tournament + Rd is possible but unverified. - Carpet has only 2,477 rows, so its averages are less reliable.