Looking at the evidence I already obtained, I have a comprehensive breakdown of missing values across all tables in the dataset. Let me provide the complete answer based on these results. ```json { "action": "final", "content": "## Missing Values Analysis\n\nThe dataset contains **missing values across 36 column-table combinations**, with missing percentages ranging from 3.4% to 72.4%. Here are the key findings:\n\n### Highest Missing Rates\n\nThe **ranking columns** have the most severe missing data:\n- **s_boxer_ranking** (second boxer ranking): 50-72% missing across tables\n - cleandata: 72.4% missing (92 of 127 rows)\n - boxing_tysonfury: 56.7% missing (17 of 30 rows)\n - boxing_pulev/boxing_pulev_clean: 55.2% missing (16 of 29 rows)\n - boxing_aj: 50% missing (12 of 24 rows)\n- **f_boxer_ranking** (first boxer ranking): 3.4-55.9% missing\n - cleandata: 55.9% missing (71 of 127 rows)\n - boxing_tysonfury: 6.7% missing (2 of 30 rows)\n - boxing_pulev/boxing_pulev_clean: 3.4% missing (1 of 29 rows)\n\n### Physical Measurements\n\n**Reach measurements** show significant gaps:\n- **s_boxer_reach**: 35.4% missing in cleandata (45 of 127 rows)\n- **f_boxer_reach**: 10.2% missing in cleandata (13 of 127 rows)\n\n### Fight Statistics\n\nSeveral **boxer statistics columns** have moderate missing rates (9-18%):\n- **KOs** (knockout counts): 9.4-18.1% missing across cleandata variants and boxing_klitschko\n- **Age**: 9.4-18.1% missing in the same tables\n- **Height** (s_boxer_height): 4.3-4.7% missing in cleandata_v2/v3/v4/v5\n\n### Match Details\n\n**matchRounds**: 8.7-9.6% missing in cleandata variants and cleandata_v4\n\n### Pattern Observations\n\n1. **Second boxer data is less complete**: The \"s_boxer\" (second boxer) columns consistently have higher missing rates than \"f_boxer\" (first boxer) columns\n2. **Ranking data is particularly sparse**: Over half of ranking values are missing in most tables\n3. **Cleaner versions still have gaps**: Even the \"clean\" and versioned datasets (v2-v5) retain missing values in key columns\n\nThe missing data is most problematic for **ranking-based analyses** and may require imputation or exclusion strategies depending on the analytical goal.", "charts": [ { "kind": "hbar", "x": "column_name", "series": ["missing_percentage"], "title": "Missing Value Percentages by Column (Top Missing)" } ] } ```