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docs: CHANGELOG + PROJECT.md for score recalibration (market_score v3 + opportunity_score v2)

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Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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Deeman
2026-02-27 06:58:48 +01:00
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CHANGELOG.md
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@@ -6,6 +6,15 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/).
## [Unreleased]
### Changed
- **Market Score v3 (Marktreife-Score recalibration)** — fixes ranking inversion where early-stage markets (Germany 1/100k) outscored mature markets (Spain 36/100k):
- **Formula rewrite** (`city_market_profile.sql`): supply development now 40 pts (log-scaled density LN(d+1)/LN(21) × count gate min(1,count/5)); demand evidence 25 pts (occupancy or 40% density proxy); population reduced to 15 pts (context); income to 10 pts (context); data quality to 10 pts; saturation discount removed
- **Count gate** eliminates small-town inflation: a single venue in a 5k-resident town can no longer outscore Berlin (was 92.7 → now 43.9 for Bernau bei Berlin)
- **LN ceiling at 20/100k** (was linear 4/100k) gives meaningful differentiation from 0 to 20: Málaga 70.1, Barcelona 67.4, Madrid 66.9, Amsterdam 58.4, Berlin 42.2, London 44.1
- **Template thresholds updated** across all 3 pSEO 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); white-space signal interplay market_score < 40 (was < 50)
- **Opportunity Score supply gap ceiling raised 4→8/100k** (`location_opportunity_profile.sql`) — gentler gradient for partially-served markets; accounts for ~87% data undercount vs FIP real-world totals. Documents discovered formula behaviour: DuckDB `LEAST(1.0, NULL)=1.0` means NULL catchment already yields full 15 pts; income PPS saturates for all EU countries; tennis courts data currently empty (formula correct, data pending)
### Added
- **Opportunity Score integration** — second scoring dimension (`Marktpotenzial`) now visible in city and country articles:
- **SQL chain**: `dim_cities` now carries `geoname_id` (from the existing GeoNames LEFT JOIN); threaded through `city_market_profile` → `pseo_city_costs_de` which LEFT JOINs `location_opportunity_profile` on `(country_code, geoname_id)`; `pseo_country_overview` gains `avg_opportunity_score`, `top_opportunity_score`, `top_opportunity_slugs`, `top_opportunity_names`