I need to explore this dataset systematically to find surprising patterns. Let me start by understanding the temporal coverage and then look for counterintuitive patterns. {"action": "tool_call", "reasoning": "First, I'll examine the temporal coverage and basic distribution patterns across both tables to understand the data landscape before identifying anomalies.", "tool": "query_sql", "args": {"sql": "SELECT 'ittf_rankings' as table_name, MIN(YearNum) as min_year, MAX(YearNum) as max_year, COUNT(DISTINCT YearNum) as distinct_years, COUNT(DISTINCT Assoc) as distinct_countries, COUNT(DISTINCT Name) as distinct_players, COUNT(*) as total_records FROM raw.ittf_rankings UNION ALL SELECT 'ittf_rankings_women', MIN(YearNum), MAX(YearNum), COUNT(DISTINCT YearNum), COUNT(DISTINCT Assoc), COUNT(DISTINCT Name), COUNT(*) FROM raw.ittf_rankings_women", "purpose": "Get temporal span and basic cardinality across both tables", "source": "raw"}}