```json { "scores": [ { "id": "cnv_b68387488726485f", "score": 92, "tier": "great", "rationale": "Concrete dataset dimensions (387K rows, 30 cols) + critical finding: no temporal data despite ingestion timestamp. Actionable for time-series analysis planning." }, { "id": "cnv_df115d2870934a10", "score": 65, "tier": "regular", "rationale": "Useful column taxonomy but lacks specificity (no distinct value counts shown, confidence medium, no evidence queries). Foundational but incomplete." }, { "id": "cnv_b6bd921af52b4813", "score": 58, "tier": "regular", "rationale": "Sample rows provide orientation but mostly restates what columns contain. Low surprise value; helps understand structure but not data patterns." }, { "id": "cnv_8998e817605043da", "score": 88, "tier": "great", "rationale": "Specific missing-value percentages (reach_B 90.2%, judge scores 86%+) with concrete impact. Critical for data cleaning decisions and analysis feasibility." }, { "id": "cnv_b03eb3ba3a9c4779", "score": 75, "tier": "regular", "rationale": "Quantified finding (7.9% duplicates, 30.7K rows) with clear deduplication recommendation. Actionable but straightforward data-quality issue." }, { "id": "cnv_46e61b57f1f14fe6", "score": 62, "tier": "regular", "rationale": "Distinct-value counts for 4 categorical columns (decision=10, result=3, stances=3 each). Useful reference but low surprise; expected for boxing data." }, { "id": "cnv_89f7d2dff5914f89", "score": 85, "tier": "great", "rationale": "Comprehensive numeric distributions with identified outliers (age -74 to 1,818 years). Reveals severe data quality issues requiring immediate investigation." }, { "id": "cnv_13464640e4824c0f", "score": 94, "tier": "great", "rationale": "Detailed data-quality audit: impossible ages, heights, weights with specific examples and impact counts. Highly actionable for data cleaning prioritization." }, { "id": "cnv_7e477a641b4f4cd8", "score": 78, "tier": "regular", "rationale": "Specific decision-type frequencies (PTS 27.9%, TKO 23.2%, KO 18.3%) and result distribution. Useful baseline but expected patterns for boxing." }, { "id": "cnv_82ae51f0e94f462b", "score": 82, "tier": "great", "rationale": "Strong correlations identified with r-values (Weight A↔B r=0.962, Height↔Reach r=0.819). Reveals weight-class matching and physical relationships; actionable for modeling." }, { "id": "cnv_71b153c82ec34a87", "score": 79, "tier": "regular", "rationale": "Identifies won_A as key metric with justification (stddev 27.6, predictive power). Shows Fighter A wins 83% but lacks deeper insight into why." }, { "id": "cnv_7101561d9f094b2a", "score": 81, "tier": "great", "rationale": "Links most important metric (won_A) to most important category (result) with clear pattern: experience correlates with match success. Directly actionable for prediction modeling." }, { "id": "cnv_76475856584445e3", "score": 86, "tier": "great", "rationale": "Multiple segmentation approaches (outcome 83% A-wins imbalance, decision types, experience levels). Reveals dataset bias and natural analytical groupings; actionable for stratified analysis." }, { "id": "cnv_1a133eca3bae442b", "score": 72, "tier": "regular", "rationale": "Clear dataset definition (boxing matches, 387K rows, fighter attributes + outcomes). Foundational orientation but lacks depth or surprising insights." }, { "id": "cnv_d241a6012c224073", "score": 89, "tier": "great", "rationale": "Counterintuitive finding: reach advantage compensates for inexperience (14.86% vs 13.28% win rates). Challenges assumptions; high surprise value and modeling implications." }, { "id": "cnv_b432fd86cf84461f", "score": 73, "tier": "regular", "rationale": "Three plausible research questions (physical advantage prediction, experience vs. skill, judge bias). Well-framed but generic; doesn't reveal new dataset insights." }, { "id": "cnv_559d79ee2c43446c", "score": 68, "tier": "regular", "rationale": "Complete column list with data types and meanings. Useful reference but low confidence (medium), no evidence queries, and mostly restates metadata." } ] } ```