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