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>
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