### 5.1 Performance Posture The infrastructure is well-positioned for current operations, with both **ittf_rankings** (271,271 rows) and **ittf_rankings_women** (202,311 rows) maintained at manageable scale. However, the absence of text-search optimization on the Name columns represents a critical gap for the most common user interaction pattern—looking up individual athletes by name. Without keyword indexing, every name-based query triggers a full table scan across nearly half a million combined records, creating unnecessary latency for coaches, analysts, and media personnel who need rapid athlete lookups during tournaments and ranking updates. ### 5.2 Key Optimizations | Target | Optimization Type | Recommendation | Strength | |--------|-------------------|----------------|----------| | ittf_rankings.Name | Text Search | keyword | High | | ittf_rankings_women.Name | Text Search | keyword | High | Implementing keyword indexing on both Name columns will deliver sub-second athlete searches regardless of dataset growth, directly improving the experience for tournament directors verifying player eligibility, journalists researching competitor histories, and federation staff managing ranking disputes. This optimization becomes increasingly valuable as historical rankings accumulate and the tables grow beyond 500,000 rows, ensuring that name-based queries—the primary access pattern for table tennis stakeholder workflows—remain performant without requiring infrastructure scaling.