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_profiles: replace dead sports culture component (10 pts,
   tennis data all zeros) with market validation signal.
   Split scored CTE into: market_scored → country_market → scored.
   country_market aggregates AVG(market_score) per country from cities
   with padel courts (market_score > 0), so zero-court locations don't
   dilute the signal. ES (~60/100) → ~6 pts. SE (~35/100) → ~3.5 pts.
   NULL → 0.5 neutral → 5 pts (untested market, not penalised).

Score budget unchanged: 25+20+30+15+10 = 100 pts.
No new models, no new data sources, no cycles.

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

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)
-  ROUND(AVG(opportunity_score), 1)                               AS avg_opportunity_score,
+  -- Opportunity score aggregates (population-weighted: saturated megacities dominate, not hundreds of small towns)
+  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,