I need to systematically check for data quality issues across different aspects of this table tennis dataset. Let me examine the evidence I already have and then probe for specific quality problems. From the evidence so far, I can see: - No null values in key fields (Date, Time, Player names, Sets) - No impossible set counts or winner mismatches in basic validation - Game columns (P1_G4, P2_G4, P1_G5, P2_G5) have significant nulls, which is expected since not all matches go to 4 or 5 games Let me check for additional quality issues: ```json { "action": "tool_call", "reasoning": "Check for date format consistency, invalid dates, and temporal ordering issues. The Date column is VARCHAR, so I need to verify it can be parsed and is in a consistent format.", "tool": "query_sql", "args": { "sql": "SELECT Date, COUNT(*) as count, MIN(Time) as earliest_time, MAX(Time) as latest_time FROM raw.table_tennis_sept2022 GROUP BY Date ORDER BY Date LIMIT 50", "purpose": "Check date format consistency and distribution" } } ```