Here are three questions the data can answer. The surface-level results I already pulled from `raw_kaggle_by_Surface` hint at each one. ## 1. How much does surface change a player's serve, and is the effect real or a product of who plays where? - **Aces per match:** grass averages about 7.6, carpet 7.9, hard 5.9 and clay 3.4. - **Double faults:** these barely move, from 2.8 on clay to 3.2 on grass. - **Serve points won (`SP`):** grass averages 84.1, against 73.1 on clay. - **Why it isn't a clean comparison:** the average player rank (`Rk`) differs a lot by surface. It is about 151 on grass, 311 on hard, 361 on clay and 517 on carpet (a lower number is a better rank). So grass matches may skew toward higher-ranked players. - **The analysis:** compare serve stats by surface while controlling for player rank and opponent rank (`vRk`). The aim is to separate the surface effect from the player-quality effect. The per-surface and per-tournament gold tables are a good starting point. ## 2. How do serve and return profiles change against stronger or weaker opponents, and which stats actually predict winning? - **Data:** `raw.serve_kaggle` holds first- and second-serve percentages, ace rate and double-fault rate. `raw.return_kaggle` holds return points won, break-point conversion and opponent rank. - **The analysis:** see where a player's serve and return numbers drop as opponent rank improves. Then test which of these stats best separates strong performers from weak ones, for example break-point conversion against first-serve points won. - **Use:** this feeds pre-match win-probability and betting models. - **Limitation:** there are no foreign keys between the tables, so `raw_kaggle`, `serve_kaggle` and `return_kaggle` can't be joined cleanly. The analysis has to be built per table, or by carefully matching on the name, date, tournament and round keys. ## 3. Does player form change across rounds, tournaments and years, and who shows "deep-run" patterns? - **Data:** the round (`Rd`) breakdowns exist across all three fact tables. `raw.players_tournament_man_` records player, year, tournament and rounds reached, and `raw.players_man_` holds career match counts. - **The analysis:** ask whether serve and return quality rise or fall in later rounds, which is partly selection because weaker players are eliminated. Compare that with how players and tournaments change over 2002–2024. - **Use:** this points to which players and tournaments are worth covering, and which players consistently outperform their ranking in deep runs. ## A data caveat that affects all three Hard and clay account for most of the match rows (117,571 and 107,650), while grass has 9,479 and carpet 2,477. Another 28 rows have no surface recorded. Grass and carpet conclusions rest on far less data, so treat surface comparisons involving them with more caution.