```json { "table_name": "ittf_rankings_women", "primary_key": "ID,YearNum,MonthNum,WeekNum", "candidates": [ { "rank": 0, "partition_key": "YearNum", "partition_key_label": "T", "partition_key_reasoning": "YearNum is the primary temporal axis with RPUV=10,115.55 (20 unique years), providing complete snapshots of all players' rankings at each time period. Top 3 coverage is 22.50%, well-distributed across years 2001-2020. This is the natural partitioning for state tables tracking rankings over time.", "clustering_key": "ID", "clustering_key_label": "X", "clustering_key_reasoning": "ID is the entity (player) being measured with RPUV=40.62 (4,980 unique players). Clustering by ID within each year groups all rankings for the same player together, enabling efficient queries for player ranking history. Uniqueness=0.025 indicates each player appears multiple times across snapshots.", "world_line": null, "session_column": null, "confidence": "HIGH", "notes": "This is a classic state table: periodic snapshots of player rankings. Each (YearNum, MonthNum, WeekNum) represents a complete ranking snapshot, and ID identifies which player. Partitioning by YearNum gives complete yearly snapshots, clustering by ID organizes players within each snapshot. The composite primary key (ID, YearNum, MonthNum, WeekNum) ensures uniqueness." }, { "rank": 1, "partition_key": "ID", "partition_key_label": "E", "partition_key_reasoning": "ID (player) with RPUV=40.62 could serve as partition key from a path perspective, tracking each player's ranking evolution over time. However, this is less natural for a state table where the primary analytical need is cross-sectional snapshots at each time period.", "clustering_key": "YearNum", "clustering_key_label": "S", "clustering_key_reasoning": "YearNum provides temporal ordering with monotonicity=0.000 (not strictly monotonic but sequential). Clustering by year within each player would order their ranking history chronologically.", "world_line": "ID", "session_column": null, "confidence": "MEDIUM", "notes": "Alternative path-oriented view: partition by player (ID) to track individual ranking trajectories over time. Less optimal than rank=0 because state tables are typically analyzed cross-sectionally (all players at time T) rather than longitudinally (one player over all T). The classifier correctly identified this as state=0.90, path=0.15." } ] } ```