I need to systematically examine data quality issues across both tables. Let me start by analyzing the specific problems I've already identified and then check for additional issues. From the evidence so far, I can see several clear problems: 1. The `height` column contains a date value "13 Dec 1916" (1 row) 2. The `country` column contains what appears to be a height measurement "75.20 inches (191 cm)" (1 row) 3. The `reach` column contains a height measurement "5.91 ft (1.80 m)" (1 row) 4. Many "Unknown" values across multiple columns Let me investigate further to identify all data quality issues. ```json { "action": "tool_call", "reasoning": "Check for outliers and impossible values in numeric measures in the fighters table - wins, losses, draws, and age (after filtering out 'Unknown')", "tool": "query_sql", "args": { "sql": "SELECT MIN(wins) as min_wins, MAX(wins) as max_wins, AVG(wins) as avg_wins, MIN(looses) as min_losses, MAX(looses) as max_losses, AVG(looses) as avg_losses, MIN(draws) as min_draws, MAX(draws) as max_draws, AVG(draws) as avg_draws, COUNT(CASE WHEN age = 'Unknown' THEN 1 END) as age_unknown, COUNT(CASE WHEN age != 'Unknown' AND TRY_CAST(age AS INTEGER) IS NOT NULL THEN 1 END) as age_numeric, MIN(CASE WHEN age != 'Unknown' AND TRY_CAST(age AS INTEGER) IS NOT NULL THEN TRY_CAST(age AS INTEGER) END) as min_age, MAX(CASE WHEN age != 'Unknown' AND TRY_CAST(age AS INTEGER) IS NOT NULL THEN TRY_CAST(age AS INTEGER) END) as max_age FROM raw.fighters", "purpose": "Identify outliers and impossible values in numeric columns of fighters table", "source": "raw" } } ```