```json { "action": "tool_call", "reasoning": "I've already identified several data quality issues from the evidence: (1) 14,661 rows in ittf_rankings and 11,224 in ittf_rankings_women have Rank=0 with non-zero Points, (2) gender mismatches (6 'W' in men's table, 21 'M' in women's table), (3) 47,662 duplicate rank periods in men's, 30,885 in women's, (4) missing months in the temporal sequence. Now I need to check for outliers in the Points distribution and examine the Previous/Previous Points relationship for consistency issues.", "tool": "query_sql", "args": { "sql": "SELECT 'ittf_rankings' as table_name, COUNT(*) as total_rows, MIN(Points) as min_points, MAX(Points) as max_points, AVG(Points) as avg_points, MEDIAN(Points) as median_points, STDDEV(Points) as stddev_points, PERCENTILE_CONT(0.95) WITHIN GROUP (ORDER BY Points) as p95_points, PERCENTILE_CONT(0.99) WITHIN GROUP (ORDER BY Points) as p99_points, COUNT(CASE WHEN Points > 15000 THEN 1 END) as extreme_high_points, COUNT(CASE WHEN Previous IS NOT NULL AND Previous Points IS NULL THEN 1 END) as prev_rank_no_prev_points, COUNT(CASE WHEN Previous IS NULL AND Previous Points IS NOT NULL THEN 1 END) as prev_points_no_prev_rank FROM raw.ittf_rankings UNION ALL SELECT 'ittf_rankings_women', COUNT(*), MIN(Points), MAX(Points), AVG(Points), MEDIAN(Points), STDDEV(Points), PERCENTILE_CONT(0.95) WITHIN GROUP (ORDER BY Points), PERCENTILE_CONT(0.99) WITHIN GROUP (ORDER BY Points), COUNT(CASE WHEN Points > 15000 THEN 1 END), COUNT(CASE WHEN Previous IS NOT NULL AND Previous Points IS NULL THEN 1 END), COUNT(CASE WHEN Previous IS NULL AND Previous Points IS NOT NULL THEN 1 END) FROM raw.ittf_rankings_women", "purpose": "Analyze Points distribution for outliers and check consistency between Previous rank and Previous Points fields", "source": "raw" } } ```