Analytics & rankings
Longitudinal performance intelligence
Twenty years of fragmented records, normalized into honest, comparable series.
The system
The source publishes walls of numbers with no verdicts. The system harvests two decades of longitudinal records and normalizes names, eras, and gaps into series a person can actually compare — on a map, ranked.
Honesty is enforced in the schema: floors are labeled as floors, coverage windows are stated, gaps stay gaps. Nothing is imputed.
The hard parts
Entity resolution across decades
A 216-entry alias map reconciles name drift across twenty years of records, so a series survives every rename and re-organization.
Zero-token harvesting
The pipeline is deterministic fetch-and-parse at polite pacing — a 530-request run completed without a single model call.
One codebase, any geography
The app is region-parameterized. The second region onboarded in days, on the same routes and the same schema.
In numbers
Next.js · MapLibre GL · Python
Happy to walk through this one properly — what it does, how it's put together, and what it would take to build something like it for you.