**The dataset has 718,470 rows across 5 tables and covers roughly August 2001 to October 2024.** **Size** | Table | Rows | |---|---| | `raw_kaggle` | 237,205 | | `return_kaggle` | 237,196 | | `serve_kaggle` | 237,185 | | `players_tournament_man_` | 6,422 | | `players_man_` | 462 | - **Rows:** the three match-level tables (`raw_kaggle`, `return_kaggle`, `serve_kaggle`) hold almost all of them, at about 237,000 each. - **Columns:** the column count depends on the source. The catalog overview I queried gives 17 columns for each of the three match tables, 4 for `players_tournament_man_` and 3 for `players_man_`. That totals 58. The dataset card lists 21, 21, 21, 8 and 7 columns for the same tables, which totals 78. I did not resolve this gap, so treat the column totals as uncertain. **Time period** - **Match dates:** `Date` is stored as text in all three match tables. After parsing, every row converted cleanly. All three tables run from 2001-08-13 to 2024-10-28, so the data spans about 23 years. - **Tournament years:** `players_tournament_man_.year` runs from 2002 to 2024 with no nulls. - **Playing years:** `players_man_.playing_years` is a text list of years per player, not a date column. - **Card ranges:** the ranges on the dataset card (for example, `raw_kaggle` starting 1-Apr-2013) come from sorting the text values alphabetically. They are not real earliest and latest dates, so I used the parsed dates above instead.