diff --git a/transform/sqlmesh_padelnomics/models/serving/location_profiles.sql b/transform/sqlmesh_padelnomics/models/serving/location_profiles.sql
index 84995ec..542a221 100644
--- a/transform/sqlmesh_padelnomics/models/serving/location_profiles.sql
+++ b/transform/sqlmesh_padelnomics/models/serving/location_profiles.sql
@@ -19,20 +19,23 @@
 --   10 pts  economic context  — income PPS normalised to 25,000 ceiling
 --   10 pts  data quality      — completeness discount
 --
--- Padelnomics Opportunity Score (Marktpotenzial-Score v7, 0–100):
+-- Padelnomics Opportunity Score (Marktpotenzial-Score v8, 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).
 --
---   v7 changes: country-level supply saturation dampener on supply deficit.
---   Saturated countries (Spain 7.4/100k) get dampened supply deficit (×0.30 → 12 pts max).
---   Emerging markets (Germany 0.24/100k) are nearly unaffected (×0.98 → ~39 pts).
---   Floor at 0.3 so supply deficit never fully vanishes.
+--   v8 changes: better spread/discrimination.
+--   - Reweight: addressable market 20→15, economic power 15→10, supply deficit 40→50.
+--   - Supply deficit existence dampener: country_venues/50 factor (0.1–1.0).
+--     Zero-venue countries get max 5 pts supply deficit (was 50).
+--   - Steeper addressable market curve: LN/500K → SQRT/1M.
+--   - NULL distance gap → 0.0 (was 0.5). Unknown = assume nearby.
+--   - Added country_percentile output column (PERCENT_RANK within country).
 --
---   20 pts  addressable market        — log-scaled catchment population, ceiling 500K
---   15 pts  economic power            — income PPS, normalised to 35,000
---   40 pts  supply deficit            — max(density gap, distance gap) × country dampener
+--   15 pts  addressable market        — sqrt-scaled catchment population, ceiling 1M
+--   10 pts  economic power            — income PPS, normalised to 35,000
+--   50 pts  supply deficit            — max(density gap, distance gap) × existence dampener
 --   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 headroom           — inverse country-level avg market maturity
@@ -228,28 +231,29 @@ country_supply AS (
 -- Step 4: add opportunity_score using country market validation + supply saturation.
 scored AS (
   SELECT ms.*,
-    -- ── Opportunity Score (Marktpotenzial-Score v7, H3 catchment) ──────────
+    -- ── Opportunity Score (Marktpotenzial-Score v8, H3 catchment) ──────────
     ROUND(
-      -- 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 (40 pts): max of density gap and distance gap.
-      -- Dampened by country-level supply saturation:
-      --   Spain (7.4/100k) → dampener 0.30 → 12 pts max
-      --   Germany (0.24/100k) → dampener 0.98 → ~39 pts max
-      + 40.0 * GREATEST(
+      -- Addressable market (15 pts): sqrt-scaled catchment population, ceiling 1M
+      15.0 * LEAST(1.0, SQRT(GREATEST(catchment_population, 1) / 1000000.0))
+      -- Economic power (10 pts): income PPS normalised to 35,000
+      + 10.0 * LEAST(1.0, COALESCE(median_income_pps, 15000) / 35000.0)
+      -- Supply deficit (50 pts): max of density gap and distance gap.
+      -- Dampened by market existence: country_venues/50 (0.1–1.0).
+      --   0 venues in country → factor 0.1 → max 5 pts supply deficit
+      --   10 venues → 0.2 → max 10 pts
+      --   50+ venues → 1.0 → full credit
+      + 50.0 * 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) / 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)
+          -- distance-based gap: 30km+ = 1.0, 0km = 0.0; NULL = 0.0 (assume nearby)
+          COALESCE(LEAST(1.0, nearest_padel_court_km / 30.0), 0.0)
         )
-        -- Country supply dampener: floor 0.3 so deficit never fully vanishes
-        * GREATEST(0.3, 1.0 - COALESCE(cs.venues_per_100k, 0.0) / 10.0)
+        -- Market existence dampener: zero-venue countries get 0.1, 50+ venues = 1.0
+        * GREATEST(0.1, LEAST(1.0, COALESCE(cs.country_venues, 0) / 50.0))
       -- 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)
@@ -301,6 +305,9 @@ SELECT
   END AS catchment_venues_per_100k,
   LEAST(GREATEST(s.market_score, 0), 100) AS market_score,
   LEAST(GREATEST(s.opportunity_score, 0), 100) AS opportunity_score,
+  ROUND(PERCENT_RANK() OVER (
+    PARTITION BY s.country_code ORDER BY s.opportunity_score
+  ) * 100, 0) AS country_percentile,
   s.median_hourly_rate,
   s.median_peak_rate,
   s.median_offpeak_rate,