```json { "scores": [ { "id": "cnv_c4015430d66e4d35", "score": 85, "tier": "great", "rationale": "Clear dataset purpose, concrete examples (Canelo, Abdugofurov), explains both tables and row meaning well." }, { "id": "cnv_e1537dcc3c7c46c2", "score": 78, "tier": "great", "rationale": "Comprehensive column inventory with data types and meanings. High substance but lacks evidence queries and result tables." }, { "id": "cnv_fb9ff5afff9a4aae", "score": 82, "tier": "great", "rationale": "Specific row/column counts, exact date range (Dec 2018–Nov 2019), clear table breakdown. Actionable orientation." }, { "id": "cnv_cfd117fc6229462a", "score": 75, "tier": "regular", "rationale": "Useful taxonomy of column types with concrete examples. No evidence queries limits confidence; medium-level actionability." }, { "id": "cnv_5983963db71c45b7", "score": 72, "tier": "regular", "rationale": "Good sample diversity illustration. Helps orient analyst but lacks surprising insights or deep analytical value." }, { "id": "cnv_4271595b34824ae9", "score": 88, "tier": "great", "rationale": "Specific missing percentages (36.18%, 17.76%, etc.), identifies actionable data quality issue. High substance and evidence." }, { "id": "cnv_ea79e22178a54f2b", "score": 70, "tier": "regular", "rationale": "Confirms dataset cleanliness (one duplicate). Useful but low-signal; expected finding for quality check." }, { "id": "cnv_f165c1c7ea774481", "score": 73, "tier": "regular", "rationale": "Distinct value counts for categorical columns. Useful reference but somewhat boilerplate; limited surprise value." }, { "id": "cnv_078b46348132461c", "score": 80, "tier": "great", "rationale": "Detailed distribution stats (min/max/mean/median/stddev) across all numeric columns. Concrete, evidence-backed, actionable." }, { "id": "cnv_edc17c123eb448ee", "score": 92, "tier": "great", "rationale": "Identifies critical data quality issues (column misalignment, impossible values, format inconsistencies). Highly actionable and surprising." }, { "id": "cnv_293034d609434f4e", "score": 81, "tier": "great", "rationale": "Specific frequency distributions (87.93% Orthodox, 26.3% US fighters). Concrete, well-evidenced, useful for segmentation." }, { "id": "cnv_6b967476c55548ac", "score": 76, "tier": "regular", "rationale": "Identifies correlations (0.67, 0.50, 0.48) but correctly notes they reflect career length, not skill. Moderate insight." }, { "id": "cnv_550eac4061a54d34", "score": 84, "tier": "great", "rationale": "Identifies KO rate as key metric with concrete examples (Gervonta Davis 95.8%, Deontay Wilder 93.2%). Well-reasoned and specific." }, { "id": "cnv_2ffe5a3f39c647e8", "score": 79, "tier": "great", "rationale": "Clear growth trend with specific periods (2015+ acceleration). Actionable for understanding data recency and coverage." }, { "id": "cnv_3724985f574a4044", "score": 77, "tier": "regular", "rationale": "Identifies 14-year gap (1988–2002) and modern concentration. Useful but somewhat expected for historical dataset." }, { "id": "cnv_15395f49b23b47da", "score": 86, "tier": "great", "rationale": "Concrete finding: Southpaw fighters average 28.5 wins vs Orthodox 18.8. Surprising, specific, actionable for analysis." }, { "id": "cnv_2d93ab51f3804b08", "score": 89, "tier": "great", "rationale": "Five natural fighter tiers with percentages and win ranges. Highly actionable segmentation with clear business logic." }, { "id": "cnv_2baf620fbdbb4978", "score": 80, "tier": "great", "rationale": "Geographic concentration data (US 26.3%, top 5 = 58.3%). Specific, well-evidenced, useful for understanding bias." }, { "id": "cnv_8b24d9d4fe4842f3", "score": 95, "tier": "great", "rationale": "Counterintuitive finding: punch power has zero predictive value despite being measured. Highly surprising, well-evidenced, decision-changing." }, { "id": "cnv_ed0473cd324e4c09", "score": 68, "tier": "regular", "rationale": "Suggests three analytical questions but lacks evidence queries or concrete findings. Useful framing but low substance." } ] } ```