**Yes. Surface is the most natural way to segment the match records, and the dataset's structure points to several other groupings.** **1. Surface (strongest segmentation, backed by the data)** In `raw.raw_kaggle`, records fall into four surfaces plus a small blank group. The segments differ clearly on serve and rank measures: | Surface | Rows | Avg aces | Avg double faults | Avg total points (TP) | Avg player rank | |---|---|---|---|---|---| | Hard | 117,571 | 5.88 | 2.97 | 148.9 | 311 | | Clay | 107,650 | 3.38 | 2.81 | 146.7 | 361 | | Grass | 9,479 | 7.57 | 3.17 | 168.4 | 151 | | Carpet | 2,477 | 7.94 | 3.11 | 146.0 | 517 | | (blank) | 28 | – | – | – | 550 | - **Hard and clay** hold about 95% of the records. Clay has the fewest aces (3.38), so it is the slow, return-friendly surface. - **Grass** has high aces (7.57) and longer matches (168 total points on average). Its average rank of 151 is much better than on the other surfaces, so it is played by a stronger field, or the data is biased toward top players there. - **Carpet** has the most aces (7.94), but it is a small, older segment of 2,477 rows with weaker-ranked players (average rank 517). - **Blank surface** is only 28 rows and is best excluded or treated as unknown. **2. Other natural groupings (from the dataset structure)** I did not run queries for these, so they rest on the dataset card rather than on computed results. - **Round (`Rd`):** about 14 values. This fits the question of whether form changes deeper into a tournament. - **Tournament:** a high-cardinality classifier. Each of the three match tables has about 3,963 distinct values, so it works better for targeted analysis than as a top-level split. - **Opponent and player rank bands:** `Rk` and `vRk` range from 1 to about 2,250. Banding them (for example top 10, top 50, or the rest) would separate strong from weak fields. - **Player and year:** `players_man_` and `players_tournament_man_` provide a player dimension (462 players) and a yearly view (2002–2024). - **Break-point situations:** the break-point conversion and saved columns (`BPCnv`, `Bpsvd`) are useful for splitting pressure points from normal ones. **Recommendation:** Use **Surface × Round** as the primary segmentation, and add rank bands to control for field strength. Since the grass and carpet segments differ in rank mix as well as in serve behaviour, compare surfaces within the same rank band rather than on raw averages. Pre-aggregated gold tables already exist for these cuts, such as `raw_kaggle_by_Surface_Rd`, `return_kaggle_by_Surface_Rk` and `serve_kaggle_by_Surface_Rd`.