From 10266c3a24d24d9689d0ad581af538d030782891 Mon Sep 17 00:00:00 2001
From: Deeman <hendriknote@gmail.com>
Date: Fri, 27 Feb 2026 06:57:57 +0100
Subject: [PATCH] =?UTF-8?q?fix(sql):=20opportunity=5Fscore=20=E2=80=94=20s?=
 =?UTF-8?q?upply=20gap=20ceiling=204=E2=86=928/100k=20+=20doc=20findings?=
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Raises supply gap ceiling from 4/100k to 8/100k in
location_opportunity_profile.sql. The original 4/100k hard cliff
truncated opportunity scores to 0 for any city with ≥4 courts/100k,
but our data undercounts ~87% of real courts (FIP: 17,300 Spanish
courts vs 2,239 in our DB). Raising to 8/100k gives a gentler gradient
and fairer partial credit when density data is incomplete.

Documents existing formula behaviour discovered during analysis:
- Income PPS: country-level constants (18k-37k range) saturate the
  /200 ceiling — all EU countries get flat 20/20 pts until city-level
  income data lands.
- Catchment NULL: DuckDB LEAST(1.0, NULL) = 1.0 (ignores nulls), so
  NULL nearest_padel_court_km already yields full 15 pts. COALESCE
  fallback is dead code but harmless.
- Tennis courts within 25km: dim_locations data is empty (all 0 rows)
  — 10-court threshold is correct for when data arrives, contributes
  0 pts everywhere for now.

Effective score impact: minimal (99% of locations have 0 courts/100k,
so supply gap was already at max). Only ~1,050 dense-court cities
see a score increase (from 0 gap pts to partial gap pts).

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
---
 .../serving/location_opportunity_profile.sql  | 26 ++++++++++++++-----
 1 file changed, 20 insertions(+), 6 deletions(-)

diff --git a/transform/sqlmesh_padelnomics/models/serving/location_opportunity_profile.sql b/transform/sqlmesh_padelnomics/models/serving/location_opportunity_profile.sql
index e848db0..1258c30 100644
--- a/transform/sqlmesh_padelnomics/models/serving/location_opportunity_profile.sql
+++ b/transform/sqlmesh_padelnomics/models/serving/location_opportunity_profile.sql
@@ -1,7 +1,7 @@
 -- Per-location padel investment opportunity intelligence.
 -- Consumed by: Gemeinde-level pSEO pages, opportunity map, "top markets" lists.
 --
--- Padelnomics Marktpotenzial-Score (0–100):
+-- Padelnomics Marktpotenzial-Score v2 (0–100):
 -- Answers "Where should I build a padel court?"
 -- Covers ALL GeoNames locations (pop ≥ 1K) — NOT filtered to existing padel markets.
 -- Zero-court locations score highest on supply gap component (white space = opportunity).
@@ -9,9 +9,21 @@
 --   25 pts  addressable market — log-scaled population, ceiling 500K
 --           (opportunity peaks in mid-size cities; megacities already served)
 --   20 pts  economic power     — country income PPS, normalised to 200
---   30 pts  supply gap         — INVERTED venue density; 0 courts/100K = full marks
---   15 pts  catchment gap      — distance to nearest padel court (>30km = full marks)
---   10 pts  sports culture     — tennis courts within 25km (≥10 = full marks)
+--                               NOTE: PPS values are country-level constants in the range
+--                               18k-37k — ALL EU countries saturate this component (20/20).
+--                               Component is a flat uplift per country until city-level
+--                               income data becomes available.
+--   30 pts  supply gap         — INVERTED venue density; 0 courts/100K = full marks.
+--                               Ceiling raised to 8/100K (was 4) for a gentler gradient
+--                               and to account for ~87% data undercount vs FIP totals.
+--                               Linear: GREATEST(0, 1 - density/8)
+--   15 pts  catchment gap      — distance to nearest padel court.
+--                               DuckDB LEAST ignores NULLs: LEAST(1.0, NULL/30) = 1.0,
+--                               so NULL nearest_km = full marks (no court in bounding box
+--                               = high opportunity). COALESCE fallback is dead code.
+--   10 pts  sports culture     — tennis courts within 25km (≥10 = full marks).
+--                               NOTE: dim_locations tennis data is empty (all 0 rows).
+--                               Component contributes 0 pts everywhere until data lands.
 
 MODEL (
   name serving.location_opportunity_profile,
@@ -50,9 +62,11 @@ SELECT
     + 20.0 * LEAST(1.0, COALESCE(l.median_income_pps, 100) / 200.0)
 
     -- Supply gap (30 pts): INVERTED venue density.
-    -- 0 courts/100K = full 30 pts (white space); ≥4/100K = 0 pts (served market).
+    -- 0 courts/100K = full 30 pts (white space); ≥8/100K = 0 pts (served market).
+    -- Ceiling raised from 4→8/100K for a gentler gradient and to account for data
+    -- undercount (~87% of real courts not in our data).
     -- This is the key signal that separates Marktpotenzial from Marktreife.
-    + 30.0 * GREATEST(0.0, 1.0 - COALESCE(l.padel_venues_per_100k, 0) / 4.0)
+    + 30.0 * GREATEST(0.0, 1.0 - COALESCE(l.padel_venues_per_100k, 0) / 8.0)
 
     -- Catchment gap (15 pts): distance to nearest existing padel court.
     -- >30km = full 15 pts (underserved catchment area).