{
    "action": "tool_call",
    "reasoning": "I have strong evidence of natural segmentation dimensions from the results so far: result (win_A/win_B/draw), decision types (10 categories), stance matchups (orthodox vs orthodox, southpaw vs southpaw, missing), age groups, and weight classes. However, I should explore one more critical dimension that often segments boxing data naturally: the experience level of fighters based on their career records (won/lost/drawn counts). This will complete the picture of natural groupings.",
    "tool": "query_sql",
    "args": {
        "sql": "WITH fighter_experience AS (SELECT CASE WHEN won_A < 5 THEN 'novice' WHEN won_A BETWEEN 5 AND 15 THEN 'developing' WHEN won_A BETWEEN 16 AND 30 THEN 'experienced' ELSE 'veteran' END AS experience_A, CASE WHEN won_B < 5 THEN 'novice' WHEN won_B BETWEEN 5 AND 15 THEN 'developing' WHEN won_B BETWEEN 16 AND 30 THEN 'experienced' ELSE 'veteran' END AS experience_B FROM raw.boxing_matches) SELECT experience_A, experience_B, COUNT(*) AS match_count, ROUND(100.0 * COUNT(*) / (SELECT COUNT(*) FROM raw.boxing_matches), 1) AS pct FROM fighter_experience GROUP BY experience_A, experience_B ORDER BY match_count DESC LIMIT 15",
        "purpose": "Identify natural segmentation by fighter experience level (career wins) to see if matches cluster by experience matchups",
        "source": "raw"
    }
}