fix: country_supply column name in location_profiles

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

CHANGELOG.md

@@ -7,6 +7,7 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/).
 ## [Unreleased]
 
 ### Changed
+- **Opportunity Score v7 → v8** — better spread and discrimination across the full 0-100 range. Addressable market weight reduced (20→15 pts) with steeper sqrt curve (ceiling 1M, was LN/500K). Economic power reduced (15→10 pts). Supply deficit increased (40→50 pts) with market existence dampener: countries with zero padel venues get max 5 pts supply deficit (factor 0.1), scaling linearly to full credit at 50+ venues. NULL nearest-court distance now treated as 0 (assume nearby) instead of 0.5. Added `country_percentile` output column (PERCENT_RANK within country). Target: P5-P95 spread ≥40 pts (was 22), zero-venue countries avg <30.
 - **Opportunity Score v6 → v7 (calibration fix)** — two fixes for inflated scores in saturated markets. (1) `dim_locations` now sources venue coordinates from `dim_venues` (deduplicated OSM + Playtomic) instead of `stg_padel_courts` (OSM only), making Playtomic-only venues visible to spatial lookups. (2) Country-level supply saturation dampener on the 40-pt supply deficit component: saturated countries (Spain ~4.5/100k) get dampened supply deficit (×0.55 → 22 pts max), emerging markets (Germany ~0.7/100k) are nearly unaffected (×0.93 → ~37 pts).
 - **Single-score simplification** — consolidated two public-facing scores (Market Score + Opportunity Score) into one **Padelnomics Score** (internally: `opportunity_score`). All maps, tooltips, article templates, and the methodology page now show a single score. Dual-ring markers reverted to single-color markers. `/market-score` route renamed to `/padelnomics-score` (old URL 301-redirects). All `mscore_*` i18n keys replaced with `pnscore_*`. Business plan queries `opportunity_score` from `location_profiles` (replaces legacy `city_market_overview` view). Map tooltip strings now i18n'd via `window.__MAP_T` (12 keys, EN + DE).
 

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
@@ -215,10 +218,10 @@ country_market AS (
 country_supply AS (
   SELECT
     country_code,
-    SUM(city_padel_venue_count)                                             AS country_venues,
+    SUM(padel_venue_count)                                                  AS country_venues,
     SUM(population)                                                         AS country_pop,
     CASE WHEN SUM(population) > 0
-         THEN SUM(city_padel_venue_count) * 100000.0 / SUM(population)
+         THEN SUM(padel_venue_count) * 100000.0 / SUM(population)
          ELSE 0
     END                                                                     AS venues_per_100k
   FROM foundation.dim_cities
@@ -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,

web/src/padelnomics/locales/de.json

@@ -1711,12 +1711,12 @@
   "pnscore_what_intro": "Der Padelnomics Score ist ein Komposit-Index von 0 bis 100, der bewertet, wie attraktiv ein Standort für eine neue Padelanlage ist. Er kombiniert angebotsseitige Lücken (gibt es genug Courts?) mit nachfrageseitigen Signalen (Bevölkerung, Einkommen, Sportaffinität) und berücksichtigt die Marktreife. Ein hoher Score bedeutet: Es gibt adressierbare Nachfrage, das Gebiet ist unterversorgt und die Rahmenbedingungen begünstigen ein Investment.",
   "pnscore_components_h2": "Was der Score misst",
   "pnscore_components_intro": "Sechs gewichtete Komponenten fließen in den Gesamtscore ein. Jede erfasst einen anderen Aspekt des Investitionspotenzials.",
-  "pnscore_cat_market_h3": "Adressierbarer Markt (20 Pkt)",
-  "pnscore_cat_market_p": "Einzugsgebiet-Bevölkerung im Umkreis von ~24 km (H3 Res-5-Zelle + Nachbarn). Logarithmisch skaliert — eine Stadt mit 500K Einwohnern erreicht das Maximum. Größeres Einzugsgebiet bedeutet mehr potenzielle Spieler.",
-  "pnscore_cat_econ_h3": "Wirtschaftskraft (15 Pkt)",
+  "pnscore_cat_market_h3": "Adressierbarer Markt (15 Pkt)",
+  "pnscore_cat_market_p": "Einzugsgebiet-Bevölkerung im Umkreis von ~24 km (H3 Res-5-Zelle + Nachbarn). Wurzelskaliert — ein Einzugsgebiet von 1 Mio. erreicht das Maximum. Größeres Einzugsgebiet bedeutet mehr potenzielle Spieler.",
+  "pnscore_cat_econ_h3": "Wirtschaftskraft (10 Pkt)",
   "pnscore_cat_econ_p": "Regionales Einkommen in Kaufkraftstandards (KKS). Höheres verfügbares Einkommen stützt Premium-Preise und häufigeres Spielen. Daten von Eurostat (EU), Census (USA), ONS (UK).",
-  "pnscore_cat_gap_h3": "Versorgungslücke (40 Pkt)",
-  "pnscore_cat_gap_p": "Die gewichtigste Komponente. Misst zwei Signale: Anlagendichte-Lücke (wie weit unter 5 Courts pro 100K?) und Entfernungslücke (wie weit zur nächsten Anlage?). Null Courts = maximale Punktzahl. Bereits gut versorgte Gebiete erhalten kaum Punkte.",
+  "pnscore_cat_gap_h3": "Versorgungslücke (50 Pkt)",
+  "pnscore_cat_gap_p": "Die gewichtigste Komponente. Misst zwei Signale: Anlagendichte-Lücke (wie weit unter 5 Courts pro 100K?) und Entfernungslücke (wie weit zur nächsten Anlage?). Gedämpft nach Marktreife — Länder mit wenigen oder keinen Padel-Anlagen erhalten reduzierten Punktwert, da eine Versorgungslücke ohne nachgewiesene Nachfrage spekulativ ist. Voller Punktwert erst ab 50+ Anlagen im Land.",
   "pnscore_cat_sports_h3": "Sportaffinität (10 Pkt)",
   "pnscore_cat_sports_p": "Tennisplatz-Dichte im Umkreis von 25 km als Proxy für Racketsport-Affinität. Regionen mit starker Tennis-Infrastruktur haben ein bereites Publikum für Padel — einen eng verwandten Sport mit niedrigerer Einstiegshürde.",
   "pnscore_cat_catchment_h3": "Baukosten-Erschwinglichkeit (5 Pkt)",

web/src/padelnomics/locales/en.json

@@ -1742,12 +1742,12 @@
   "pnscore_what_intro": "The Padelnomics Score is a 0-100 composite index that evaluates how attractive a location is for a new padel facility. It combines supply-side gaps (are there enough courts?) with demand-side signals (population, income, sports culture) and adjusts for market maturity. A high score means: there is addressable demand, the area is underserved, and conditions favor a new investment.",
   "pnscore_components_h2": "What It Measures",
   "pnscore_components_intro": "Six weighted components combine into the final score. Each captures a different aspect of investment potential.",
-  "pnscore_cat_market_h3": "Addressable Market (20 pts)",
-  "pnscore_cat_market_p": "Catchment population within ~24 km (H3 res-5 cell + neighbors). Log-scaled — a city of 500K scores the maximum. Larger catchment means more potential players.",
-  "pnscore_cat_econ_h3": "Economic Power (15 pts)",
+  "pnscore_cat_market_h3": "Addressable Market (15 pts)",
+  "pnscore_cat_market_p": "Catchment population within ~24 km (H3 res-5 cell + neighbors). Square-root scaled — a catchment of 1M scores the maximum. Larger catchment means more potential players.",
+  "pnscore_cat_econ_h3": "Economic Power (10 pts)",
   "pnscore_cat_econ_p": "Regional income in purchasing power standard (PPS). Higher disposable income supports premium pricing and more frequent play. Data from Eurostat (EU), Census (US), ONS (UK).",
-  "pnscore_cat_gap_h3": "Supply Deficit (40 pts)",
-  "pnscore_cat_gap_p": "The single biggest component. Measures two signals: court density gap (how far below 5 courts per 100K?) and distance gap (how far to the nearest existing court?). Zero courts = maximum score. Already well-served areas score near zero.",
+  "pnscore_cat_gap_h3": "Supply Deficit (50 pts)",
+  "pnscore_cat_gap_p": "The single biggest component. Measures two signals: court density gap (how far below 5 courts per 100K?) and distance gap (how far to the nearest existing court?). Dampened by market existence — countries with few or no padel venues get reduced credit, since a supply gap without proven demand is speculative. Full credit requires 50+ venues nationally.",
   "pnscore_cat_sports_h3": "Sports Culture (10 pts)",
   "pnscore_cat_sports_p": "Tennis court density within 25 km as a proxy for racquet sport adoption. Regions with strong tennis infrastructure have a ready audience for padel — a closely related sport with a lower barrier to entry.",
   "pnscore_cat_catchment_h3": "Construction Affordability (5 pts)",