Here are the three questions I think this dataset answers best. The surface figures come from the gold surface aggregate I queried. The other two are proposals I haven't run yet. ## 1. How much does surface change a player's serve, and is it a real effect or a selection effect? The surface profiles differ sharply: - **Aces per match:** Carpet averages about 7.9 and Grass about 7.6. Hard is about 5.9 and Clay only about 3.4. - **Serve points per match:** Grass averages about 84 and Clay about 73. - **Double faults:** These barely move, from about 2.8 on Clay to about 3.2 on Grass. Grass and Carpet help the serve, but this is a mix of surface and who plays there. The average player rank (`Rk`) on Grass is about 151, against about 361 on Clay. Lower numbers mean better-ranked players. Grass matches are therefore dominated by top players, so part of the serve advantage comes from who is on court. Controlling for `Rk` and `vRk` would separate the two. The `return_kaggle_by_Surface_Rk` and `raw_kaggle_by_Surface` gold tables are the natural starting points. ## 2. Does a player's serve and return profile change against higher- or lower-ranked opponents, and does it shift across rounds? Every match row has the player's rank (`Rk`) and the opponent's rank (`vRk`). The serve table holds first- and second-serve percentages, ace and double-fault rates, and break points saved. The return table holds return points won and break-point conversion. The gold tables already cut these by round, rank and opponent. This supports questions like: - Which skills drop most when facing a top-10 opponent? - Is break-point conversion a stable skill or mostly luck? - Do players serve better or worse in later rounds? This speaks directly to scouting and pre-match modelling. ## 3. Which players and tournaments show the most consistent form, and how has play changed over time? `players_tournament_man_` records each player, year and tournament from 2002 to 2024, including the rounds reached. `players_man_` gives career match counts and playing years. The match tables carry dates, so you could track: - how serve dominance (aces and first-serve points won) has trended by year - which players over- or under-perform at specific tournaments - how deep a tournament run predicts later results Two caveats apply here: - The serve table starts in 2006, but the raw and return tables start in 2013. Long-run trends should use the serve table only. - The five tables share no detected join key, so each analysis has to be done table by table, or by matching on player name, date and tournament. Matching on those keys is not verified. ## Data caveats - The match counts are very uneven by surface: Hard has 117,571 rows, Clay 107,650, Grass 9,479 and Carpet only 2,477. Grass and Carpet findings are much less certain than Hard and Clay. - 28 rows have no surface label.