88ed17484b
Fixes ranking inversion where Germany (1/100k courts) outscored Spain (36/100k). Root causes: population/income were 55% of max before any padel signal, density ceiling saturated 73% of cities, small-town inflation (1 venue / 5k pop = 20/100k = full marks), and the saturation discount actively penalised mature markets. SQL (city_market_profile.sql): - Supply development 40pts: log-scaled density LN(d+1)/LN(21) × count gate min(1, count/5). Ceiling 20/100k. Count gate kills small-town inflation without hard cutoffs (1 venue = 20%, 5+ = 100%). - Demand evidence 25pts: occupancy if available; 40% density proxy otherwise. Separated from supply to avoid double-counting. - Addressable market 15pts: population as context, not maturity. - Economic context 10pts: income PPS (flat per country, low signal). - Data quality 10pts. - Removed saturation discount. High density = maturity. Verified spot-check scores: Málaga (46v, 7.77/100k): 70.1 [was 98.9] Barcelona (104v, 6.17/100k): 67.4 [was 100.0] Amsterdam (24v, 3.24/100k): 58.4 [was 93.7] Bernau bei Berlin (2v, 5.74/100k): 43.9 [was 92.7] Berlin (20v, 0.55/100k): 42.2 [was 74.1] London (66v, 0.74/100k): 44.1 [was 75.5] Templates (city-cost-de, country-overview, city-pricing): - Color coding: green >= 55 (was 65), amber >= 35 (was 40) - Intro/FAQ tiers: strong >= 55 (was 70), mid >= 35 (was 45) - Opportunity interplay: market_score < 40 (was < 50) for white-space Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
serving
Analytics-ready views consumed by the web app and programmatic SEO.
Query these from analytics.py via DuckDB read-only connection.
Naming convention: serving.<purpose> (e.g. serving.city_market_profile)