544891611f
Two targeted fixes for inflated country scores (ES 83, SE 77): 1. pseo_country_overview: replace AVG() with population-weighted averages for avg_opportunity_score and avg_market_score. Madrid/Barcelona now dominate Spain's average instead of hundreds of 30K-town white-space towns. Expected ES drop from ~83 to ~55-65. 2. location_profiles: replace dead sports culture component (10 pts, tennis data all zeros) with market validation signal. Split scored CTE into: market_scored → country_market → scored. country_market aggregates AVG(market_score) per country from cities with padel courts (market_score > 0), so zero-court locations don't dilute the signal. ES (~60/100) → ~6 pts. SE (~35/100) → ~3.5 pts. NULL → 0.5 neutral → 5 pts (untested market, not penalised). Score budget unchanged: 25+20+30+15+10 = 100 pts. No new models, no new data sources, no cycles. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
45 lines
2.6 KiB
SQL
45 lines
2.6 KiB
SQL
-- pSEO article data: per-country padel market overview.
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-- One row per country — consumed by the country-overview.md.jinja template.
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-- Aggregates city-level data from pseo_city_costs_de.
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--
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-- top_city_slugs / top_city_names are ordered lists (up to 5) used to generate
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-- internal links from the country hub to its top city pages.
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MODEL (
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name serving.pseo_country_overview,
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kind FULL,
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cron '@daily',
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grain country_slug
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);
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SELECT
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country_code,
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country_name_en,
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country_slug,
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COUNT(*) AS city_count,
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SUM(padel_venue_count) AS total_venues,
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-- Population-weighted: large cities (Madrid, Barcelona) dominate, not hundreds of small towns
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ROUND(SUM(market_score * population) / NULLIF(SUM(population), 0), 1) AS avg_market_score,
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MAX(market_score) AS top_city_market_score,
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-- Top 5 cities by venue count (prominence), then score for internal linking
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LIST(city_slug ORDER BY padel_venue_count DESC, market_score DESC NULLS LAST)[1:5] AS top_city_slugs,
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LIST(city_name ORDER BY padel_venue_count DESC, market_score DESC NULLS LAST)[1:5] AS top_city_names,
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-- Opportunity score aggregates (population-weighted: saturated megacities dominate, not hundreds of small towns)
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ROUND(SUM(opportunity_score * population) / NULLIF(SUM(population), 0), 1) AS avg_opportunity_score,
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MAX(opportunity_score) AS top_opportunity_score,
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-- Top 5 opportunity cities by population (prominence), then opportunity score
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LIST(city_slug ORDER BY population DESC, opportunity_score DESC NULLS LAST)[1:5] AS top_opportunity_slugs,
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LIST(city_name ORDER BY population DESC, opportunity_score DESC NULLS LAST)[1:5] AS top_opportunity_names,
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-- Pricing medians across cities (NULL when no Playtomic coverage in country)
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ROUND(MEDIAN(median_hourly_rate), 0) AS median_hourly_rate,
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ROUND(MEDIAN(median_peak_rate), 0) AS median_peak_rate,
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ROUND(MEDIAN(median_offpeak_rate), 0) AS median_offpeak_rate,
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-- Use the most common currency in the country (MIN is deterministic for single-currency countries)
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MIN(price_currency) AS price_currency,
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SUM(population) AS total_population,
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CURRENT_DATE AS refreshed_date
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FROM serving.pseo_city_costs_de
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GROUP BY country_code, country_name_en, country_slug
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-- Only countries with enough cities to be worth a hub page
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HAVING COUNT(*) >= 2
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