I need to compute distribution statistics for the numeric columns in this dataset. From the evidence, I can see the `fighters` table has several numeric columns (wins, looses, draws), and I know from the card that `popular_matches` has many numeric columns as well. However, I notice some data quality issues from step-1: the `age`, `height`, `reach`, and `country` columns have problematic ranges (e.g., age ranges from "0" to "Unknown", height shows dates like "13 Dec 1916"). The `ko_rate` is VARCHAR with percentage strings. I need to focus on the truly numeric columns. Let me write a comprehensive query to get distribution statistics for all numeric columns across both tables in one go. ```json { "action": "tool_call", "reasoning": "I need to compute min, max, mean, median, and standard deviation for all numeric columns. From the card, the truly numeric columns are: fighters (wins, looses, draws) and popular_matches (opponent_1_estimated_punch_power, opponent_2_estimated_punch_power, opponent_1_estimated_punch_resistance, opponent_2_estimated_punch_resistance, opponent_1_estimated_ability_to_take_punch, opponent_2_estimated_ability_to_take_punch, opponent_1_rounds_boxed, opponent_2_rounds_boxed, opponent_1_round_ko_percentage, opponent_2_round_ko_percentage, opponent_1_has_been_ko_percentage, opponent_2_has_been_ko_percentage, opponent_1_avg_weight, opponent_2_avg_weight). I'll compute all statistics in one query using UNION ALL to combine results from both tables.", "tool": "query_sql", "args": { "sql": "SELECT 'fighters.wins' AS column_name, MIN(wins) AS min_val, MAX(wins) AS max_val, AVG(wins) AS mean_val, MEDIAN(wins) AS median_val, STDDEV(wins) AS stddev_val FROM raw.fighters UNION ALL SELECT 'fighters.looses', MIN(looses), MAX(looses), AVG(looses), MEDIAN(looses), STDDEV(looses) FROM raw.fighters UNION ALL SELECT 'fighters.draws', MIN(draws), MAX(draws), AVG(draws), MEDIAN(draws), STDDEV(draws) FROM raw.fighters UNION ALL SELECT 'popular_matches.opponent_1_estimated_punch_power', MIN(opponent_1_estimated_punch_power), MAX(opponent_1_estimated_punch_power), AVG(opponent_1_estimated_punch_power), MEDIAN(opponent_1_estimated_punch_power), STDDEV(opponent_1_estimated_punch_power) FROM raw.popular_matches UNION ALL SELECT 'popular_matches.opponent_2_estimated_punch_power', MIN(opponent_2_estimated_punch_power), MAX(opponent_2_estimated_punch_power), AVG(opponent_2_estimated_punch_power), MEDIAN(opponent_2_estimated_punch_power), STDDEV(opponent_2_estimated_punch_power) FROM raw.popular_matches UNION ALL SELECT 'popular_matches.opponent_1_estimated_punch_resistance', MIN(opponent_1_estimated_punch_resistance), MAX(opponent_1_estimated_punch_resistance), AVG(opponent_1_estimated_punch_resistance), MEDIAN(opponent_1_estimated_punch_resistance), STDDEV(opponent_1_estimated_punch_resistance) FROM raw.popular_matches UNION ALL SELECT 'popular_matches.opponent_2_estimated_punch_resistance', MIN(opponent_2_estimated_punch_resistance), MAX(opponent_2_estimated_punch_resistance), AVG(opponent_2_estimated_punch_resistance), MEDIAN(opponent_2_estimated_punch_resistance), STDDEV(opponent_2_estimated_punch_resistance) FROM raw.popular_matches UNION ALL SELECT 'popular_matches.opponent_1_estimated_ability_to_take_punch', MIN(opponent_1_estimated_ability_to_take_punch), MAX(opponent_1_estimated_ability_to_take_punch), AVG(opponent_1_estimated_ability_to_take_punch), MEDIAN(opponent_1_estimated_ability_to_take_punch), STDDEV(opponent_1_estimated_ability_to_take_punch) FROM raw.popular_matches UNION ALL SELECT 'popular_matches.opponent_2_estimated_ability_to_take_punch', MIN(opponent_2_estimated_ability_to_take_punch), MAX(opponent_2_estimated_ability_to_take_punch), AVG(opponent_2_estimated_ability_to_take_punch), MEDIAN(opponent_2_estimated_ability_to_take_punch), STDDEV(opponent_2_estimated_ability_to_take_punch) FROM raw.popular_matches UNION ALL SELECT 'popular_matches.opponent_1_rounds_boxed', MIN(opponent_1_rounds_boxed), MAX(opponent_1_rounds_boxed), AVG(opponent_1_rounds_boxed), MEDIAN(opponent_1_rounds_boxed), STDDEV(opponent_1_rounds_boxed) FROM raw.popular_matches UNION ALL SELECT 'popular_matches.opponent_2_rounds_boxed', MIN(opponent_2_rounds_boxed), MAX(opponent_2_rounds_boxed), AVG(opponent_2_rounds_boxed), MEDIAN(opponent_2_rounds_boxed), STDDEV(opponent_2_rounds_boxed) FROM raw.popular_matches UNION ALL SELECT 'popular_matches.opponent_1_round_ko_percentage', MIN(opponent_1_round_ko_percentage), MAX(opponent_1_round_ko_percentage), AVG(opponent_1_round_ko_percentage), MEDIAN(opponent_1_round_ko_percentage), STDDEV(opponent_1_round_ko_percentage) FROM raw.popular_matches UNION ALL SELECT 'popular_matches.opponent_2_round_ko_percentage', MIN(opponent_2_round_ko_percentage), MAX(opponent_2_round_ko_percentage), AVG(opponent_2_round_ko_percentage), MEDIAN(opponent_2_round_ko_percentage), STDDEV(opponent_2_round_ko_percentage) FROM raw.popular_matches UNION ALL SELECT 'popular_matches.opponent_1_has_been_ko_percentage', MIN(opponent_1_has_been_ko_percentage), MAX(opponent_1_has_been_ko_percentage), AVG(opponent_1_has_been_ko_percentage), MEDIAN(opponent_1_has_been_ko_percentage), STDDEV(opponent_1_has_been_ko_percentage) FROM raw.popular_matches UNION ALL SELECT 'popular_matches.opponent_2_has_been_ko_percentage', MIN(opponent_2_has_been_ko_percentage), MAX(opponent_2_has_been_ko_percentage), AVG(opponent_2_has_been_ko_percentage), MEDIAN(opponent_2_has_been_ko_percentage), STDDEV(opponent_2_has_been_ko_percentage) FROM raw.popular_matches UNION ALL SELECT 'popular_matches.opponent_1_avg_weight', MIN(opponent_1_avg_weight), MAX(opponent_1_avg_weight), AVG(opponent_1_avg_weight), MEDIAN(opponent_1_avg_weight), STDDEV(opponent_1_avg_weight) FROM raw.popular_matches UNION ALL SELECT 'popular_matches.opponent_2_avg_weight', MIN(opponent_2_avg_weight), MAX(opponent_2_avg_weight), AVG(opponent_2_avg_weight), MEDIAN(opponent_2_avg_weight), STDDEV(opponent_2_avg_weight) FROM raw.popular_matches", "purpose": "Compute distribution statistics (min, max, mean, median, standard deviation) for all numeric columns across both tables", "source": "raw" } } ```