Looking at the evidence I've already gathered, I have the distinct counts for all categorical columns across both tables from my previous queries. ## Distinct Values in Categorical Columns ### ittf_rankings table - **Assoc** (Association/Country): 180 distinct values - **Gender**: 3 distinct values - **Name** (Player name): 6,610 distinct values - **Rank**: 1,792 distinct values ### ittf_rankings_women table - **Assoc** (Association/Country): 166 distinct values - **Gender**: 3 distinct values - **Name** (Player name): 4,974 distinct values - **Rank**: 1,380 distinct values **Key observations:** - Both tables track 3 gender categories (likely M, W, and possibly a mixed/team category) - The men's rankings (ittf_rankings) cover more countries (180 vs 166) and more unique players (6,610 vs 4,974) - The men's rankings also have a wider rank range (1,792 distinct ranks vs 1,380 for women) - These counts represent the unique values that appear across the entire 2001-2020 time period covered by the dataset