### 1.2 Key Data Assets | Table | Role | Rows | % of Total | Size | |-------|------|-----:|----------:|-----:| | fighters | dimension | 2,760 | 94.8% | 0.2 MB | | popular_matches | dimension | 152 | 5.2% | 0.0 MB | The **fighters** table dominates this dataset, representing 95% of all records and serving as the primary repository of boxer attributes and career statistics. The **popular_matches** table captures a curated subset of 152 notable bouts, likely representing high-profile contests or a training sample for outcome prediction modeling. Together these assets provide the raw material for analyzing which fighter characteristics—physical attributes, experience metrics, or performance history—correlate with victory. ### 1.1 Business Data Landscape This dataset supports boxing match outcome prediction by cataloging fighter profiles and a selection of historical bouts. The **fighters** dimension table anchors the collection with 2,760 individual boxer records, while **popular_matches** provides 152 contest snapshots that pair opponents and record results. The structure reflects a sports analytics use case: one table inventories the athletes and their measurable traits, the other documents competitive events where those traits were tested. With 2,912 total rows across 0.2 MB, this is a compact, purpose-built dataset designed for predictive modeling rather than comprehensive league management or real-time event tracking. ### 1.3 Data Integration Posture No formal relationships connect these two tables, leaving them operationally siloed despite their logical interdependence. Analysts seeking to correlate fighter attributes with match outcomes must manually bridge the gap—likely matching boxer identifiers between **fighters** and **popular_matches**—which introduces risk of misalignment and limits the dataset's readiness for automated cross-functional analytics. The absence of validated joins means every predictive model or performance dashboard will require custom integration logic, slowing iteration and increasing the chance that fighter statistics and match records drift out of sync as the dataset evolves.