diff --git a/transform/sqlmesh_padelnomics/models/serving/location_profiles.sql b/transform/sqlmesh_padelnomics/models/serving/location_profiles.sql
index 5d5f36e..9e5483b 100644
--- a/transform/sqlmesh_padelnomics/models/serving/location_profiles.sql
+++ b/transform/sqlmesh_padelnomics/models/serving/location_profiles.sql
@@ -19,22 +19,22 @@
 --   10 pts  economic context  — income PPS normalised to 25,000 ceiling
 --   10 pts  data quality      — completeness discount
 --
--- Padelnomics Opportunity Score (Marktpotenzial-Score v5, 0–100):
+-- Padelnomics Opportunity Score (Marktpotenzial-Score v6, 0–100):
 --   "Where should I build a padel court?"
 --   Computed for ALL locations — zero-court locations score highest on supply deficit.
 --   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.
+--   v6 changes: lower density ceiling 8→5/100k (saturated markets hit zero-gap sooner),
+--   increase supply deficit weight 35→40 pts, reduce addressable market 25→20 pts,
+--   invert market validation (high country maturity = LESS opportunity).
 --
---   25 pts  addressable market        — log-scaled catchment population, ceiling 500K
+--   20 pts  addressable market        — log-scaled catchment population, ceiling 500K
 --   15 pts  economic power            — income PPS, normalised to 35,000
---   35 pts  supply deficit            — max(density gap, distance gap); eliminates double-count
+--   40 pts  supply deficit            — max(density gap, distance gap); eliminates double-count
 --   10 pts  sports culture            — tennis court density as racquet-sport adoption proxy
 --    5 pts  construction affordability — income relative to construction costs (PLI)
---   10 pts  market validation         — country-level avg market maturity (from market_scored CTE)
+--   10 pts  market headroom           — inverse country-level avg market maturity
 --
 -- Consumers query directly with WHERE filters:
 --   cities API:       WHERE country_slug = ? AND city_slug IS NOT NULL
@@ -198,9 +198,9 @@ market_scored AS (
     END AS market_score
   FROM with_pricing
 ),
--- Step 2: country-level avg market maturity — used as market validation signal (10 pts).
+-- Step 2: country-level avg market maturity — used as market headroom signal (10 pts).
 -- Filter to market_score > 0 (cities with padel courts only) so zero-court locations
--- don't dilute the country signal. ES proven demand → ~60, SE struggling → ~35.
+-- don't dilute the country signal. Higher avg = more saturated = less headroom.
 country_market AS (
   SELECT
     country_code,
@@ -212,21 +212,21 @@ country_market AS (
 -- Step 3: add opportunity_score using country market validation signal.
 scored AS (
   SELECT ms.*,
-    -- ── Opportunity Score (Marktpotenzial-Score v5, H3 catchment) ──────────
+    -- ── Opportunity Score (Marktpotenzial-Score v6, 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))
+      -- Addressable market (20 pts): log-scaled catchment population, ceiling 500K
+      20.0 * LEAST(1.0, LN(GREATEST(catchment_population, 1)) / LN(500000))
       -- Economic power (15 pts): income PPS normalised to 35,000
       + 15.0 * LEAST(1.0, COALESCE(median_income_pps, 15000) / 35000.0)
-      -- Supply deficit (35 pts): max of density gap and distance gap.
-      -- 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
+      -- Supply deficit (40 pts): max of density gap and distance gap.
+      -- Ceiling 5/100k (down from 8): Spain at 6-16/100k now hits zero-gap.
+      + 40.0 * GREATEST(
+          -- density-based gap (H3 catchment): 0 courts = 1.0, 5/100k = 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) / 5.0),
           -- distance-based gap: 30km+ = 1.0, 0km = 0.0; NULL = 0.5
           COALESCE(LEAST(1.0, nearest_padel_court_km / 30.0), 0.5)
         )
@@ -239,10 +239,11 @@ scored AS (
           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.
-      -- ES (~70/100): proven demand → ~7 pts. SE (~35/100): emerging → ~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)
+      -- Market headroom (10 pts): INVERSE country-level avg market maturity.
+      -- High avg market score = saturated market = LESS opportunity for new entrants.
+      -- ES (~46/100): proven demand, less headroom → ~5.4 pts.
+      -- SE (~40/100): emerging → ~6 pts. NULL: 0.5 neutral → 5 pts.
+      + 10.0 * (1.0 - COALESCE(cm.country_avg_market_score / 100.0, 0.5))
     , 1) AS opportunity_score
   FROM market_scored ms
   LEFT JOIN country_market cm ON ms.country_code = cm.country_code