I need to analyze the data quality issues in this boxing matches dataset. I already have comprehensive statistics for age, height, and reach columns. Let me examine what these reveal and check other aspects. Looking at the evidence, I can see several concerning patterns: **Age columns (age_A, age_B):** - Negative minimum values (-74) are impossible for ages - Maximum values (1818, 2016) appear to be years rather than ages - This suggests mixed data: some entries contain ages, others contain birth years **Height columns (height_A, height_B):** - Minimum of 2 and 15 cm are impossibly short for adult boxers - Maximum of 255 cm is impossibly tall - These likely represent data entry errors or mixed units **Reach columns (reach_A, reach_B):** - Minimum values (5, 1 cm) are impossibly short - Maximum of 456 cm is impossibly long - Similar issues to height Let me check the other numeric columns and examine the categorical fields: ```json { "action": "tool_call", "reasoning": "Need to profile the weight, record (won/lost/drawn/kos), and judge scoring columns to check for outliers and impossible values", "tool": "query_sql", "args": { "sql": "SELECT COUNT(*) as total_rows, COUNT(weight_A) as weight_A_count, MIN(weight_A) as weight_A_min, MAX(weight_A) as weight_A_max, AVG(weight_A) as weight_A_avg, MEDIAN(weight_A) as weight_A_median, COUNT(weight_B) as weight_B_count, MIN(weight_B) as weight_B_min, MAX(weight_B) as weight_B_max, AVG(weight_B) as weight_B_avg, MEDIAN(weight_B) as weight_B_median, MIN(won_A) as won_A_min, MAX(won_A) as won_A_max, MIN(won_B) as won_B_min, MAX(won_B) as won_B_max, MIN(lost_A) as lost_A_min, MAX(lost_A) as lost_A_max, MIN(kos_A) as kos_A_min, MAX(kos_A) as kos_A_max, MIN(judge1_A) as judge1_A_min, MAX(judge1_A) as judge1_A_max, MIN(judge2_A) as judge2_A_min, MAX(judge2_A) as judge2_A_max FROM raw.boxing_matches", "purpose": "Profile weight, fight records, and judge scoring columns for outliers and impossible values", "source": "raw" } } ```