feat(scoring): Opportunity Score v4 → v5 — fix correlated components

- Merge supply gap (30pts) + catchment gap (15pts) → supply deficit (35pts, GREATEST)
  Eliminates ~80% correlated double-count on a single signal.
- Add sports culture signal (10pts): tennis court density as racquet-sport adoption proxy.
  Ceiling 50 courts/25km. Harmless when tennis data is zero (contributes 0).
- Add construction affordability (5pts): income relative to PLI construction costs.
  Joins dim_countries.pli_construction. High income + low build cost = high score.
- Reduce economic power from 20 → 15pts to make room.

New weights: addressable market 25, economic power 15, supply deficit 35,
sports culture 10, construction affordability 5, market validation 10.

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

transform/sqlmesh_padelnomics/models/serving/location_profiles.sql

@@ -19,19 +19,22 @@
 --   10 pts  economic context  — income PPS normalised to 25,000 ceiling
 --   10 pts  data quality      — completeness discount
 --
--- Padelnomics Opportunity Score (Marktpotenzial-Score v4, 0–100):
+-- Padelnomics Opportunity Score (Marktpotenzial-Score v5, 0–100):
 --   "Where should I build a padel court?"
---   Computed for ALL locations — zero-court locations score highest on supply gap.
+--   Computed for ALL locations — zero-court locations score highest on supply deficit.
---   H3 catchment methodology: addressable market and supply gap use a regional
+--   H3 catchment methodology: addressable market and supply deficit use a regional
 --   H3 catchment (res-5 cell + 6 neighbours, ~24km radius).
 --
+--   v5 changes: merge supply gap + catchment gap → single supply deficit (35 pts),
+--   add sports culture proxy (10 pts, tennis density), add construction affordability (5 pts),
+--   reduce economic power from 20 → 15 pts.
+--
 --   25 pts  addressable market        — log-scaled catchment population, ceiling 500K
---   20 pts  economic power     — income PPS, normalised to 35,000
+--   15 pts  economic power            — income PPS, normalised to 35,000
---   30 pts  supply gap         — inverted catchment venue density; 0 courts = full marks
+--   35 pts  supply deficit            — max(density gap, distance gap); eliminates double-count
---   15 pts  catchment gap      — distance to nearest padel court
+--   10 pts  sports culture            — tennis court density as racquet-sport adoption proxy
---   10 pts  market validation  — country-level avg market maturity (from market_scored CTE).
+--    5 pts  construction affordability — income relative to construction costs (PLI)
---                               Replaces sports culture proxy (v3: tennis data was all zeros).
+--   10 pts  market validation         — country-level avg market maturity (from market_scored CTE)
---                               ES (~60/100) → ~6 pts, SE (~35/100) → ~3.5 pts, unknown → 5 pts.
 --
 -- Consumers query directly with WHERE filters:
 --   cities API:       WHERE country_slug = ? AND city_slug IS NOT NULL
@@ -110,7 +113,7 @@ city_match AS (
     ORDER BY c.padel_venue_count DESC
   ) = 1
 ),
--- Pricing / occupancy from Playtomic (via city_slug) + H3 catchment
+-- Pricing / occupancy from Playtomic (via city_slug) + H3 catchment + country PLI
 with_pricing AS (
   SELECT
     b.*,
@@ -123,6 +126,7 @@ with_pricing AS (
     vpb.median_occupancy_rate,
     vpb.median_daily_revenue_per_venue,
     vpb.price_currency,
+    dc.pli_construction,
     COALESCE(ct.catchment_population, b.population)::BIGINT           AS catchment_population,
     COALESCE(ct.catchment_padel_courts, b.padel_venue_count)::INTEGER AS catchment_padel_courts
   FROM base b
@@ -134,6 +138,8 @@ with_pricing AS (
     AND cm.city_slug = vpb.city_slug
   LEFT JOIN catchment ct
     ON b.geoname_id = ct.geoname_id
+  LEFT JOIN foundation.dim_countries dc
+    ON b.country_code = dc.country_code
 ),
 -- Step 1: market score only — needed first so we can aggregate country averages.
 market_scored AS (
@@ -206,23 +212,35 @@ country_market AS (
 -- Step 3: add opportunity_score using country market validation signal.
 scored AS (
   SELECT ms.*,
-    -- ── Opportunity Score (Marktpotenzial-Score v4, H3 catchment) ──────────
+    -- ── Opportunity Score (Marktpotenzial-Score v5, H3 catchment) ──────────
     ROUND(
       -- Addressable market (25 pts): log-scaled catchment population, ceiling 500K
       25.0 * LEAST(1.0, LN(GREATEST(catchment_population, 1)) / LN(500000))
-      -- Economic power (20 pts): income PPS normalised to 35,000
+      -- Economic power (15 pts): income PPS normalised to 35,000
-      + 20.0 * LEAST(1.0, COALESCE(median_income_pps, 15000) / 35000.0)
+      + 15.0 * LEAST(1.0, COALESCE(median_income_pps, 15000) / 35000.0)
-      -- Supply gap (30 pts): inverted catchment venue density
+      -- Supply deficit (35 pts): max of density gap and distance gap.
-      + 30.0 * GREATEST(0.0, 1.0 - COALESCE(
+      -- Merges old supply gap (30) + catchment gap (15) which were ~80% correlated.
+      + 35.0 * GREATEST(
+          -- density-based gap (H3 catchment): 0 courts = 1.0, 8/100k = 0.0
+          GREATEST(0.0, 1.0 - COALESCE(
             CASE WHEN catchment_population > 0
               THEN GREATEST(catchment_padel_courts, COALESCE(city_padel_venue_count, 0))::DOUBLE / catchment_population * 100000
               ELSE 0.0
-          END, 0.0) / 8.0)
+            END, 0.0) / 8.0),
-      -- Catchment gap (15 pts): distance to nearest court
+          -- distance-based gap: 30km+ = 1.0, 0km = 0.0; NULL = 0.5
-      + 15.0 * COALESCE(LEAST(1.0, nearest_padel_court_km / 30.0), 0.5)
+          COALESCE(LEAST(1.0, nearest_padel_court_km / 30.0), 0.5)
+        )
+      -- Sports culture (10 pts): tennis density as racquet-sport adoption proxy.
+      -- Ceiling 50 courts within 25km. Harmless when tennis data is zero (contributes 0).
+      + 10.0 * LEAST(1.0, COALESCE(tennis_courts_within_25km, 0) / 50.0)
+      -- Construction affordability (5 pts): income purchasing power relative to build costs.
+      -- PLI construction is EU27=100 index. High income + low construction cost = high score.
+      + 5.0 * LEAST(1.0,
+          COALESCE(median_income_pps, 15000) / 35000.0
+          / GREATEST(0.5, COALESCE(pli_construction, 100.0) / 100.0)
+        )
       -- Market validation (10 pts): country-level avg market maturity.
-      -- Replaces sports culture (v3 tennis data was all zeros = dead code).
+      -- ES (~70/100): proven demand → ~7 pts. SE (~35/100): emerging → ~3.5 pts.
-      -- ES (~60/100): proven demand → ~6 pts. SE (~35/100): struggling → ~3.5 pts.
       -- 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