I need to explore this table tennis dataset more deeply to identify the most analytically interesting questions it can answer. Let me start by examining the column details and then look at some gold tables to understand what pre-computed aggregations exist. ```json { "action": "tool_call", "reasoning": "I need to understand the full column structure and data characteristics of the table_tennis_sept2022 table to identify what analytical questions are possible. The card shows 22 columns including player names, set scores, game scores, dates, times, and a HomeWinner flag. I'll inspect the columns to see their distributions and quality.", "tool": "inspect_columns", "args": { "table": "table_tennis_sept2022" } } ```