```json { "action": "tool_call", "reasoning": "I have column metadata for both tables. Now I need to check for data quality issues systematically. I'll start by examining the key measures (Points, Previous Points, Rank, Previous) for outliers and impossible values, checking for negative values, extreme outliers, and logical inconsistencies (e.g., Previous rank > current rank when points increased). I'll query both tables together to compare patterns.", "tool": "query_sql", "args": { "sql": "SELECT 'ittf_rankings' as table_name, COUNT(*) as total_rows, COUNT(CASE WHEN Points < 0 THEN 1 END) as negative_points, COUNT(CASE WHEN \"Previous Points\" < 0 THEN 1 END) as negative_prev_points, COUNT(CASE WHEN Rank < 0 THEN 1 END) as negative_rank, COUNT(CASE WHEN Previous < 0 THEN 1 END) as negative_prev_rank, COUNT(CASE WHEN Rank = 0 THEN 1 END) as zero_rank, COUNT(CASE WHEN Points = 0 AND Rank > 0 AND Rank <= 100 THEN 1 END) as zero_points_top100, COUNT(CASE WHEN Points > 0 AND Rank = 0 THEN 1 END) as points_but_rank_zero, MAX(Points) as max_points, MAX(Rank) as max_rank, COUNT(CASE WHEN WeekNum > 53 THEN 1 END) as invalid_week, COUNT(CASE WHEN MonthNum > 12 THEN 1 END) as invalid_month FROM raw.ittf_rankings UNION ALL SELECT 'ittf_rankings_women', COUNT(*), COUNT(CASE WHEN Points < 0 THEN 1 END), COUNT(CASE WHEN \"Previous Points\" < 0 THEN 1 END), COUNT(CASE WHEN Rank < 0 THEN 1 END), COUNT(CASE WHEN Previous < 0 THEN 1 END), COUNT(CASE WHEN Rank = 0 THEN 1 END), COUNT(CASE WHEN Points = 0 AND Rank > 0 AND Rank <= 100 THEN 1 END), COUNT(CASE WHEN Points > 0 AND Rank = 0 THEN 1 END), MAX(Points), MAX(Rank), COUNT(CASE WHEN WeekNum > 53 THEN 1 END), COUNT(CASE WHEN MonthNum > 12 THEN 1 END) FROM raw.ittf_rankings_women", "purpose": "Check for impossible values: negative points/ranks, zero ranks with points, invalid week/month numbers, and identify max values for outlier assessment" } } ```