Looking at the evidence, I can see the birth year distribution across all players in the dataset. The results show a clear temporal pattern with several notable features. Let me get the complete picture by querying for summary statistics to identify the key inflection points: ```json { "action": "tool_call", "reasoning": "I have the full birth year distribution from step-8 (ordered DESC). Now I need to identify the specific peaks, valleys, and growth phases. I'll calculate decade-level aggregates and identify the top/bottom years to pinpoint the notable spikes and dips.", "tool": "query_sql", "args": { "sql": "SELECT FLOOR(\"Birth year\" / 10) * 10 AS decade, COUNT(*) AS player_count FROM raw.ittf_player_info WHERE \"Birth year\" > 0 GROUP BY decade ORDER BY decade", "purpose": "Aggregate by decade to identify major growth phases and structural breaks in the time series" } } ```