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# 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.