feat(transform): opportunity score v4 — market validation + population-weighted aggregation

Two targeted fixes for inflated country scores (ES 83, SE 77):

1. pseo_country_overview: replace AVG() with population-weighted averages
   for avg_opportunity_score and avg_market_score. Madrid/Barcelona now
   dominate Spain's average instead of hundreds of 30K-town white-space
   towns. Expected ES drop from ~83 to ~55-65.

2. location_opportunity_profile: replace dead sports culture component
   (10 pts, tennis data all zeros) with market validation signal.
   New country_market CTE aggregates city_market_profile per country_code.
   ES (~60/100) → ~6 pts (proven demand). SE (~35/100) → ~3.5 pts
   (struggling market). NULL → 0.5 neutral → 5 pts (untested market).

Score budget unchanged: 25+20+30+15+10 = 100 pts.
New dependency: location_opportunity_profile → serving.city_market_profile (no cycle).

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

transform/sqlmesh_padelnomics/models/serving/location_opportunity_profile.sql

@@ -1,7 +1,7 @@
 -- Per-location padel investment opportunity intelligence.
 -- Consumed by: Gemeinde-level pSEO pages, opportunity map, "top markets" lists.
 --
--- Padelnomics Marktpotenzial-Score v3 (0–100):
+-- Padelnomics Marktpotenzial-Score v4 (0–100):
 -- Answers "Where should I build a padel court?"
 -- Covers ALL GeoNames locations (pop ≥ 1K) — NOT filtered to existing padel markets.
 -- Zero-court locations score highest on supply gap component (white space = opportunity).
@@ -30,9 +30,10 @@
 --                               DuckDB LEAST ignores NULLs: LEAST(1.0, NULL/30) = 1.0,
 --                               so NULL nearest_km = full marks (no court in bounding box
 --                               = high opportunity). COALESCE fallback is dead code.
---   10 pts  sports culture     — tennis courts within 25km (≥10 = full marks).
+--   10 pts  market validation  — country-level avg market maturity score (from city_market_profile).
---                               NOTE: dim_locations tennis data is empty (all 0 rows).
+--                               Replaces sports culture proxy (v3: tennis data was all zeros).
---                               Component contributes 0 pts everywhere until data lands.
+--                               ES (~60/100) → 6 pts, SE (~35/100) → 3.5 pts, unknown → 5 pts (neutral).
+--                               Distinguishes proven demand (ES white-space) from failing markets (SE).
 
 MODEL (
   name serving.location_opportunity_profile,
@@ -53,6 +54,16 @@ hex_stats AS (
   FROM foundation.dim_locations
   GROUP BY h3_cell_res5
 ),
+-- Country-level average market maturity — used as market validation signal (10 pts).
+-- Replaces sports culture proxy (tennis data was all zeros).
+-- Proven demand markets (ES ~60) reward white-space towns more than failing markets (SE ~35).
+country_market AS (
+  SELECT
+    country_code,
+    ROUND(AVG(market_score), 1) AS country_avg_market_score
+  FROM serving.city_market_profile
+  GROUP BY country_code
+),
 -- For each location, sum hex_stats across the cell + 6 neighbours (k_ring=1).
 -- Effective catchment: ~24km radius — realistic driving distance.
 catchment AS (
@@ -124,12 +135,14 @@ SELECT
     -- NULL = no courts found anywhere (rare edge case) → neutral 0.5.
     + 15.0 * COALESCE(LEAST(1.0, l.nearest_padel_court_km / 30.0), 0.5)
 
-    -- Sports culture proxy (10 pts): tennis courts within 25km.
+    -- Market validation (10 pts): country-level avg market maturity (from city_market_profile).
-    -- ≥10 courts = full 10 pts (proven racket sport market = faster padel adoption).
+    -- Replaces sports culture proxy (v3 tennis data was all zeros = dead code).
-    -- 0 courts = 0 pts. Many new padel courts open inside existing tennis clubs.
+    -- ES (~60/100): proven demand → ~6 pts. SE (~35/100): struggling → ~3.5 pts.
-    + 10.0 * LEAST(1.0, l.tennis_courts_within_25km / 10.0)
+    -- NULL (no courts in country yet): 0.5 neutral → 5 pts (untested, not penalised).
+    + 10.0 * COALESCE(cm.country_avg_market_score / 100.0, 0.5)
   , 1)                                                                   AS opportunity_score,
   CURRENT_DATE                                                           AS refreshed_date
 FROM foundation.dim_locations l
 LEFT JOIN catchment c ON c.geoname_id = l.geoname_id
+LEFT JOIN country_market cm ON cm.country_code = l.country_code
 ORDER BY opportunity_score DESC

transform/sqlmesh_padelnomics/models/serving/pseo_country_overview.sql

@@ -18,13 +18,14 @@ SELECT
   country_slug,
   COUNT(*)                                                        AS city_count,
   SUM(padel_venue_count)                                         AS total_venues,
-  ROUND(AVG(market_score), 1)                                    AS avg_market_score,
+  -- Population-weighted: large cities (Madrid, Barcelona) dominate, not hundreds of small towns
+  ROUND(SUM(market_score * population) / NULLIF(SUM(population), 0), 1) AS avg_market_score,
   MAX(market_score)                                              AS top_city_market_score,
   -- Top 5 cities by venue count (prominence), then score for internal linking
   LIST(city_slug ORDER BY padel_venue_count DESC, market_score DESC NULLS LAST)[1:5]     AS top_city_slugs,
   LIST(city_name ORDER BY padel_venue_count DESC, market_score DESC NULLS LAST)[1:5]     AS top_city_names,
-  -- Opportunity score aggregates (NULL-safe: cities without geoname_id match excluded from AVG)
+  -- Opportunity score aggregates (population-weighted: saturated megacities dominate, not hundreds of small towns)
-  ROUND(AVG(opportunity_score), 1)                               AS avg_opportunity_score,
+  ROUND(SUM(opportunity_score * population) / NULLIF(SUM(population), 0), 1) AS avg_opportunity_score,
   MAX(opportunity_score)                                         AS top_opportunity_score,
   -- Top 5 opportunity cities by population (prominence), then opportunity score
   LIST(city_slug ORDER BY population DESC, opportunity_score DESC NULLS LAST)[1:5] AS top_opportunity_slugs,