### 3.1 Quality Grade The **Table Tennis Sept2022** dataset earns an **A (Excellent)** grade with a 94% overall quality score, reflecting strong structural integrity and near-complete data capture across most match attributes. With 100% referential integrity—structurally guaranteed in this single-table design—and 89% completeness across 7,851 match records, the dataset provides a reliable foundation for performance analysis, tournament reporting, and player ranking calculations. Business stakeholders can confidently use this data for operational dashboards and historical trend analysis, though two specific columns require attention before the dataset supports comprehensive fifth-game analytics. ### 3.2 Key Risk Areas | Risk Type | Detail | Severity | |-----------|--------|----------| | Missing Data | table_tennis_sept2022.P1_G5 (68% null) | High | | Missing Data | table_tennis_sept2022.P2_G5 (68% null) | High | The dataset's only material quality concern centers on **fifth-game score tracking**, where both `P1_G5` and `P2_G5` columns show 68% null values. This pattern indicates that roughly two-thirds of matches concluded in three or four games, making the missing data structurally expected rather than a collection failure. However, for the 32% of matches that *did* reach a fifth game, any additional nulls would distort competitive balance metrics and clutch-performance analysis. These columns must be validated before building any analytics that measure player performance under decisive-game pressure. ### 3.3 Remediation Priorities - **Audit fifth-game completeness**: Cross-reference the 32% of records with non-null `P1_G5` or `P2_G5` values against match metadata (tournament type, round) to confirm all decisive fifth games were captured; flag any matches showing four completed games but missing fifth-game scores for manual review. - **Document match-length business rules**: Establish clear metadata indicating whether null fifth-game scores represent "match ended earlier" versus "data not collected," enabling analysts to filter confidently for best-of-five completion rates and preventing misinterpretation in executive reports. - **Enrich with match outcome flags**: Add a calculated `Match_Length` column (3, 4, or 5 games) and `Went_To_Fifth` boolean to eliminate repeated null-checking logic in downstream reports, improving query performance and reducing analyst error when segmenting by match intensity. - **Validate player identifier consistency**: Confirm that player names in `P1_Name` and `P2_Name` use standardized spelling and formatting across all 7,851 records to prevent duplicate player profiles in ranking systems and performance dashboards.