diff --git a/transform/sqlmesh_padelnomics/models/serving/city_market_profile.sql b/transform/sqlmesh_padelnomics/models/serving/city_market_profile.sql
index 3b14ec6..9956094 100644
--- a/transform/sqlmesh_padelnomics/models/serving/city_market_profile.sql
+++ b/transform/sqlmesh_padelnomics/models/serving/city_market_profile.sql
@@ -1,16 +1,18 @@
-- One Big Table: per-city padel market intelligence.
-- Consumed by: SEO article generation, planner city-select pre-fill, API endpoints.
--
--- Padelnomics Marktreife-Score v2 (0–100):
+-- Padelnomics Marktreife-Score v3 (0–100):
-- Answers "How mature/established is this padel market?"
-- Only computed for cities with ≥1 padel venue (padel_venue_count > 0).
-- For white-space opportunity scoring, see serving.location_opportunity_profile.
--
--- 30 pts population — log-scaled to 1M+ city ceiling
--- 25 pts income PPS — normalised to 200 ceiling (covers CH/NO/LU outliers)
--- 30 pts demand — observed occupancy if available, else venue density
--- 15 pts data quality — completeness discount, not a market signal
--- ×0.85 saturation — discount when venues_per_100k > 8 (oversupplied market)
+-- 40 pts supply development — log-scaled density (LN ceiling 20/100k) × count gate
+-- (min(1, count/5) kills small-town inflation)
+-- 25 pts demand evidence — occupancy when available; 40% density proxy otherwise
+-- 15 pts addressable market — log-scaled population, ceiling 1M (context only)
+-- 10 pts economic context — income PPS normalised to 200 ceiling
+-- 10 pts data quality — completeness discount
+-- No saturation discount: high density = maturity, not a penalty
MODEL (
name serving.city_market_profile,
@@ -61,28 +63,29 @@ WITH base AS (
scored AS (
SELECT *,
ROUND(
- -- Population (30 pts): log-scale, 1M+ city = full marks.
- -- LN(1) = 0 so unpopulated cities score 0 here — they still score on demand.
- 30.0 * LEAST(1.0, LN(GREATEST(population, 1)) / LN(1000000))
- -- Economic power (25 pts): income PPS normalised to 200 ceiling.
- -- 200 covers high-income outliers (CH ~190, NO ~180, LU ~200+).
- -- Drives pricing power and willingness-to-pay directly.
- + 25.0 * LEAST(1.0, COALESCE(median_income_pps, 100) / 200.0)
- -- Demand evidence (30 pts): observed occupancy is the best signal
- -- (proves real demand). If unavailable, venue density is the proxy
- -- (proves market exists; caps at 4/100K to avoid penalising dense cities).
- + 30.0 * CASE
+ -- Supply development (40 pts): THE maturity signal.
+ -- Log-scaled density: LN(density+1)/LN(21) → 20/100k ≈ full marks.
+ -- Count gate: min(1, count/5) — 1 venue=20%, 5+ venues=100%.
+ -- Kills small-town inflation (1 court / 5k pop = 20/100k) without hard cutoffs.
+ 40.0 * LEAST(1.0, LN(COALESCE(venues_per_100k, 0) + 1) / LN(21))
+ * LEAST(1.0, padel_venue_count / 5.0)
+ -- Demand evidence (25 pts): occupancy when Playtomic data available.
+ -- Fallback: 40% of density score (avoids double-counting with supply component).
+ + 25.0 * CASE
WHEN median_occupancy_rate IS NOT NULL
THEN LEAST(1.0, median_occupancy_rate / 0.65)
- ELSE LEAST(1.0, COALESCE(venues_per_100k, 0) / 4.0)
+ ELSE 0.4 * LEAST(1.0, LN(COALESCE(venues_per_100k, 0) + 1) / LN(21))
+ * LEAST(1.0, padel_venue_count / 5.0)
END
- -- Data quality (15 pts): measures completeness, not market quality.
- -- Reduced from 20pts — kept as confidence discount, not market signal.
- + 15.0 * data_confidence
+ -- Addressable market (15 pts): population as context, not maturity signal.
+ -- LN(1) = 0 so zero-pop cities score 0 here.
+ + 15.0 * LEAST(1.0, LN(GREATEST(population, 1)) / LN(1000000))
+ -- Economic context (10 pts): country-level income PPS.
+ -- Flat per country — kept as context modifier, not primary signal.
+ + 10.0 * LEAST(1.0, COALESCE(median_income_pps, 100) / 200.0)
+ -- Data quality (10 pts): completeness discount.
+ + 10.0 * data_confidence
, 1)
- -- Saturation discount: venues_per_100k > 8 signals oversupply.
- -- ~8/100K ≈ Spain-tier density; above this marginal return decreases.
- * CASE WHEN venues_per_100k > 8 THEN 0.85 ELSE 1.0 END
AS market_score
FROM base
)
diff --git a/web/src/padelnomics/content/templates/city-cost-de.md.jinja b/web/src/padelnomics/content/templates/city-cost-de.md.jinja
index cc1b032..6f4f8c3 100644
--- a/web/src/padelnomics/content/templates/city-cost-de.md.jinja
+++ b/web/src/padelnomics/content/templates/city-cost-de.md.jinja
@@ -21,7 +21,7 @@ priority_column: population
padelnomics Market Score
-
{{ market_score | round(1) }}/100
+
{{ market_score | round(1) }}/100
{% if opportunity_score %}
@@ -39,7 +39,7 @@ priority_column: population
-{{ city_name }} erreicht einen **padelnomics Market Score von {{ market_score | round(1) }}/100** — damit liegt die Stadt{% if market_score >= 70 %} unter den stärksten Padel-Märkten in {{ country_name_en }}{% elif market_score >= 45 %} im soliden Mittelfeld der Padel-Märkte in {{ country_name_en }}{% else %} in einem frühen Padel-Markt mit Wachstumspotenzial{% endif %}. Aktuell gibt es **{{ padel_venue_count }} Padelanlagen** für {% if population >= 1000000 %}{{ (population / 1000000) | round(1) }}M{% else %}{{ (population / 1000) | round(0) | int }}K{% endif %} Einwohner — das entspricht {{ venues_per_100k | round(1) }} Anlagen pro 100.000 Einwohner.{% if opportunity_score %} Der **padelnomics Opportunity Score von {{ opportunity_score | round(1) }}/100** bewertet das Investitionspotenzial — Versorgungslücken, Einzugsgebiet und Sportaffinität der Region:{% if opportunity_score >= 65 and market_score < 50 %} überschaubare Konkurrenz trifft auf starkes Standortpotenzial{% elif opportunity_score >= 65 %} hohes Potenzial trotz bereits aktivem Marktumfeld{% elif opportunity_score >= 40 %} solides Potenzial, der Markt beginnt sich zu verdichten{% else %} der Standort ist vergleichsweise gut versorgt, Differenzierung wird zum Schlüssel{% endif %}.{% endif %}
+{{ city_name }} erreicht einen **padelnomics Market Score von {{ market_score | round(1) }}/100** — damit liegt die Stadt{% if market_score >= 55 %} unter den stärksten Padel-Märkten in {{ country_name_en }}{% elif market_score >= 35 %} im soliden Mittelfeld der Padel-Märkte in {{ country_name_en }}{% else %} in einem frühen Padel-Markt mit Wachstumspotenzial{% endif %}. Aktuell gibt es **{{ padel_venue_count }} Padelanlagen** für {% if population >= 1000000 %}{{ (population / 1000000) | round(1) }}M{% else %}{{ (population / 1000) | round(0) | int }}K{% endif %} Einwohner — das entspricht {{ venues_per_100k | round(1) }} Anlagen pro 100.000 Einwohner.{% if opportunity_score %} Der **padelnomics Opportunity Score von {{ opportunity_score | round(1) }}/100** bewertet das Investitionspotenzial — Versorgungslücken, Einzugsgebiet und Sportaffinität der Region:{% if opportunity_score >= 65 and market_score < 40 %} überschaubare Konkurrenz trifft auf starkes Standortpotenzial{% elif opportunity_score >= 65 %} hohes Potenzial trotz bereits aktivem Marktumfeld{% elif opportunity_score >= 40 %} solides Potenzial, der Markt beginnt sich zu verdichten{% else %} der Standort ist vergleichsweise gut versorgt, Differenzierung wird zum Schlüssel{% endif %}.{% endif %}
Die entscheidende Frage für Investoren: Was bringt ein Padel-Investment bei den aktuellen Preisen, Auslastungsraten und Baukosten tatsächlich? Das Finanzmodell unten rechnet mit echten Marktdaten aus {{ city_name }}.
@@ -102,7 +102,7 @@ Eine detaillierte Preisanalyse mit Preisspannen und Vergleichsdaten findest Du a
Ist {{ city_name }} ein guter Standort für eine Padelhalle?
-{{ city_name }} erreicht **{{ market_score | round(1) }}/100** auf dem padelnomics Market Score, der Bevölkerungsgröße, Anlagendichte und Datenqualität berücksichtigt. {% if market_score >= 70 %}Ein Score über 70 signalisiert einen starken Markt: große Bevölkerung, wachsende Anlagenzahl und belastbare Preisdaten. {% elif market_score >= 45 %}Ein mittlerer Score bedeutet solide Grundlagen, aber einen teils stärker umkämpften oder datenlimitierten Markt. {% else %}Ein niedrigerer Score spricht für eine kleinere Stadt, begrenzte Datenlage oder einen Markt im Aufbau — was gleichzeitig weniger Wettbewerb und First-Mover-Vorteile bedeuten kann. {% endif %}Mit dem [Finanzplaner](/{{ language }}/planner) kannst Du Deine eigenen Annahmen durchrechnen.
+{{ city_name }} erreicht **{{ market_score | round(1) }}/100** auf dem padelnomics Market Score, der Anlagendichte, Bevölkerungsgröße und Datenqualität berücksichtigt. {% if market_score >= 55 %}Ein Score über 55 signalisiert einen starken Markt: etablierte Anlagendichte, wachsende Spielerbasis und belastbare Preisdaten. {% elif market_score >= 35 %}Ein mittlerer Score bedeutet solide Grundlagen, aber einen teils stärker umkämpften oder datenlimitierten Markt. {% else %}Ein niedrigerer Score spricht für einen Markt im frühen Aufbau — was gleichzeitig weniger Wettbewerb und First-Mover-Vorteile bedeuten kann. {% endif %}Mit dem [Finanzplaner](/{{ language }}/planner) kannst Du Deine eigenen Annahmen durchrechnen.
@@ -161,7 +161,7 @@ Der **Market Score ({{ market_score | round(1) }}/100)** misst die *Marktreife*:
padelnomics Market Score
-
{{ market_score | round(1) }}/100
+
{{ market_score | round(1) }}/100
{% if opportunity_score %}
@@ -179,7 +179,7 @@ Der **Market Score ({{ market_score | round(1) }}/100)** misst die *Marktreife*:
-{{ city_name }} has a **padelnomics Market Score of {{ market_score | round(1) }}/100** — placing it{% if market_score >= 70 %} among the strongest padel markets in {{ country_name_en }}{% elif market_score >= 45 %} in the mid-tier of {{ country_name_en }}'s padel markets{% else %} in an early-stage padel market with room for growth{% endif %}. The city currently has **{{ padel_venue_count }} padel venues** serving a population of {% if population >= 1000000 %}{{ (population / 1000000) | round(1) }}M{% else %}{{ (population / 1000) | round(0) | int }}K{% endif %} residents — a density of {{ venues_per_100k | round(1) }} venues per 100,000 people.{% if opportunity_score %} The **padelnomics Opportunity Score of {{ opportunity_score | round(1) }}/100** scores investment potential — supply gaps, catchment reach, and sports culture as a demand proxy:{% if opportunity_score >= 65 and market_score < 50 %} limited competition meets strong location fundamentals{% elif opportunity_score >= 65 %} strong potential despite an already active market{% elif opportunity_score >= 40 %} solid potential as the market starts to fill in{% else %} the area is comparatively well-served; differentiation is the key lever{% endif %}.{% endif %}
+{{ city_name }} has a **padelnomics Market Score of {{ market_score | round(1) }}/100** — placing it{% if market_score >= 55 %} among the strongest padel markets in {{ country_name_en }}{% elif market_score >= 35 %} in the mid-tier of {{ country_name_en }}'s padel markets{% else %} in an early-stage padel market with room for growth{% endif %}. The city currently has **{{ padel_venue_count }} padel venues** serving a population of {% if population >= 1000000 %}{{ (population / 1000000) | round(1) }}M{% else %}{{ (population / 1000) | round(0) | int }}K{% endif %} residents — a density of {{ venues_per_100k | round(1) }} venues per 100,000 people.{% if opportunity_score %} The **padelnomics Opportunity Score of {{ opportunity_score | round(1) }}/100** scores investment potential — supply gaps, catchment reach, and sports culture as a demand proxy:{% if opportunity_score >= 65 and market_score < 40 %} limited competition meets strong location fundamentals{% elif opportunity_score >= 65 %} strong potential despite an already active market{% elif opportunity_score >= 40 %} solid potential as the market starts to fill in{% else %} the area is comparatively well-served; differentiation is the key lever{% endif %}.{% endif %}
The question investors actually need answered is: given current pricing, occupancy, and build costs, what does the return look like? The financial model below uses real {{ city_name }} market data to give you that answer.
@@ -242,7 +242,7 @@ For a detailed pricing breakdown with price ranges and venue comparisons, see th
Is {{ city_name }} a good location for a padel center?
-{{ city_name }} scores **{{ market_score | round(1) }}/100** on the padelnomics Market Score, which accounts for population size, existing venue density, and data completeness. {% if market_score >= 70 %}A score above 70 indicates a strong market: high population, growing venue count, and solid pricing data. {% elif market_score >= 45 %}A mid-range score means decent fundamentals but a more competitive or data-limited market. {% else %}A lower score reflects either a smaller city, sparse venue data, or an early-stage market — which can also mean lower competition and first-mover advantage. {% endif %}Use the [Padelnomics planner](/{{ language }}/planner) to model your specific assumptions.
+{{ city_name }} scores **{{ market_score | round(1) }}/100** on the padelnomics Market Score, which primarily reflects venue density alongside population size and data completeness. {% if market_score >= 55 %}A score above 55 indicates a strong market: established venue density, a growing player base, and solid pricing data. {% elif market_score >= 35 %}A mid-range score means decent fundamentals but a more competitive or data-limited market. {% else %}A lower score reflects an early-stage market — which can also mean lower competition and first-mover advantage. {% endif %}Use the [Padelnomics planner](/{{ language }}/planner) to model your specific assumptions.
diff --git a/web/src/padelnomics/content/templates/city-pricing.md.jinja b/web/src/padelnomics/content/templates/city-pricing.md.jinja
index abaf6cc..baf380f 100644
--- a/web/src/padelnomics/content/templates/city-pricing.md.jinja
+++ b/web/src/padelnomics/content/templates/city-pricing.md.jinja
@@ -55,7 +55,7 @@ Die Preisspanne von {{ hourly_rate_p25 | round(0) | int }} bis {{ hourly_rate_p7
## Wie steht {{ city_name }} im Vergleich da?
-{{ city_name }} hat {{ padel_venue_count }} Padelanlagen für {% if population >= 1000000 %}{{ (population / 1000000) | round(1) }}M{% else %}{{ (population / 1000) | round(0) | int }}K{% endif %} Einwohner ({{ venues_per_100k | round(1) }} Anlagen pro 100K Einwohner). {% if market_score >= 65 %}Mit einem padelnomics Market Score von {{ market_score | round(1) }}/100 gehört {{ city_name }} zu den stärksten Padel-Märkten in {{ country_name_en }} — höhere Auslastung und Preise sind typisch für dichte, etablierte Märkte. {% elif market_score >= 40 %}Ein Market Score von {{ market_score | round(1) }}/100 steht für einen Markt im Aufbau: genug Angebot für marktgerechte Preise, aber Raum für neue Anlagen. {% else %}Ein Market Score von {{ market_score | round(1) }}/100 deutet auf einen Markt in der Frühphase hin, in dem sich Preise und Auslastung mit dem Wachstum des Sports noch deutlich entwickeln können. {% endif %}
+{{ city_name }} hat {{ padel_venue_count }} Padelanlagen für {% if population >= 1000000 %}{{ (population / 1000000) | round(1) }}M{% else %}{{ (population / 1000) | round(0) | int }}K{% endif %} Einwohner ({{ venues_per_100k | round(1) }} Anlagen pro 100K Einwohner). {% if market_score >= 55 %}Mit einem padelnomics Market Score von {{ market_score | round(1) }}/100 gehört {{ city_name }} zu den stärksten Padel-Märkten in {{ country_name_en }} — höhere Auslastung und Preise sind typisch für dichte, etablierte Märkte. {% elif market_score >= 35 %}Ein Market Score von {{ market_score | round(1) }}/100 steht für einen Markt im Aufbau: genug Angebot für marktgerechte Preise, aber Raum für neue Anlagen. {% else %}Ein Market Score von {{ market_score | round(1) }}/100 deutet auf einen Markt in der Frühphase hin, in dem sich Preise und Auslastung mit dem Wachstum des Sports noch deutlich entwickeln können. {% endif %}
Die Anlagendichte von {{ venues_per_100k | round(1) }} pro 100K Einwohner beeinflusst die Preisgestaltung direkt: {% if venues_per_100k >= 3.0 %}Höhere Dichte bedeutet mehr Wettbewerb, was die Preise eher stabilisiert oder senkt.{% elif venues_per_100k >= 1.0 %}Moderate Dichte ermöglicht marktgerechte Preise bei gleichzeitigem Wachstumsspielraum.{% else %}Niedrige Dichte gibt Betreibern mehr Preissetzungsmacht — vorausgesetzt, die Nachfrage ist da.{% endif %}
@@ -168,7 +168,7 @@ The P25–P75 price range of {{ hourly_rate_p25 | round(0) | int }} to {{ hourly
## How Does {{ city_name }} Compare?
-{{ city_name }} has {{ padel_venue_count }} padel venues for a population of {% if population >= 1000000 %}{{ (population / 1000000) | round(1) }}M{% else %}{{ (population / 1000) | round(0) | int }}K{% endif %} ({{ venues_per_100k | round(1) }} venues per 100K residents). {% if market_score >= 65 %}With a padelnomics Market Score of {{ market_score | round(1) }}/100, {{ city_name }} is one of the stronger padel markets in {{ country_name_en }} — higher occupancy and pricing typically follow dense, competitive markets. {% elif market_score >= 40 %}A market score of {{ market_score | round(1) }}/100 reflects a mid-tier market: enough supply to have competitive pricing, but room for new venues to grow. {% else %}A market score of {{ market_score | round(1) }}/100 indicates an early-stage market where pricing and occupancy benchmarks may shift as the sport grows. {% endif %}
+{{ city_name }} has {{ padel_venue_count }} padel venues for a population of {% if population >= 1000000 %}{{ (population / 1000000) | round(1) }}M{% else %}{{ (population / 1000) | round(0) | int }}K{% endif %} ({{ venues_per_100k | round(1) }} venues per 100K residents). {% if market_score >= 55 %}With a padelnomics Market Score of {{ market_score | round(1) }}/100, {{ city_name }} is one of the stronger padel markets in {{ country_name_en }} — higher occupancy and pricing typically follow dense, competitive markets. {% elif market_score >= 35 %}A market score of {{ market_score | round(1) }}/100 reflects a mid-tier market: enough supply to have competitive pricing, but room for new venues to grow. {% else %}A market score of {{ market_score | round(1) }}/100 indicates an early-stage market where pricing and occupancy benchmarks may shift as the sport grows. {% endif %}
Venue density of {{ venues_per_100k | round(1) }} per 100K residents directly influences pricing: {% if venues_per_100k >= 3.0 %}higher density means more competition, which tends to stabilize or compress prices.{% elif venues_per_100k >= 1.0 %}moderate density supports market-rate pricing with room for growth.{% else %}low density gives operators more pricing power — provided demand exists.{% endif %}
diff --git a/web/src/padelnomics/content/templates/country-overview.md.jinja b/web/src/padelnomics/content/templates/country-overview.md.jinja
index c8a5074..72682e2 100644
--- a/web/src/padelnomics/content/templates/country-overview.md.jinja
+++ b/web/src/padelnomics/content/templates/country-overview.md.jinja
@@ -26,7 +26,7 @@ priority_column: total_venues
padelnomics Market Score
-
{{ avg_market_score }}/100
+
{{ avg_market_score }}/100
{% if avg_opportunity_score %}
@@ -40,15 +40,15 @@ priority_column: total_venues
-In {{ country_name_en }} erfassen wir aktuell **{{ total_venues }} Padelanlagen** in **{{ city_count }} Städten**. Der durchschnittliche padelnomics Market Score liegt bei **{{ avg_market_score }}/100**{% if avg_market_score >= 65 %} — ein starker Markt mit breiter Infrastruktur und belastbaren Preisdaten{% elif avg_market_score >= 40 %} — ein wachsender Markt mit guter Abdeckung{% else %} — ein aufstrebender Markt, in dem Früheinsteiger noch Premiumstandorte sichern können{% endif %}.
+In {{ country_name_en }} erfassen wir aktuell **{{ total_venues }} Padelanlagen** in **{{ city_count }} Städten**. Der durchschnittliche padelnomics Market Score liegt bei **{{ avg_market_score }}/100**{% if avg_market_score >= 55 %} — ein starker Markt mit breiter Infrastruktur und belastbaren Preisdaten{% elif avg_market_score >= 35 %} — ein wachsender Markt mit guter Abdeckung{% else %} — ein aufstrebender Markt, in dem Früheinsteiger noch Premiumstandorte sichern können{% endif %}.
## Marktlandschaft
Padel wächst in {{ country_name_en }} mit bemerkenswertem Tempo. Unsere Daten zeigen {{ total_venues }} erfasste Anlagen — eine Zahl, die angesichts nicht auf Buchungsplattformen gelisteter Clubs vermutlich noch höher liegt. Der durchschnittliche padelnomics Market Score von {{ avg_market_score }}/100 über {{ city_count }} Städte spiegelt sowohl die Marktreife als auch die Datenverfügbarkeit wider.
-{% if avg_market_score >= 65 %}Märkte mit Scores über 65 weisen in der Regel eine etablierte Spielerbasis, belastbare Preisdaten und berechenbare Nachfragemuster auf — entscheidend für eine solide Finanzplanung. Dennoch bleiben viele Städte unterversorgt: Selbst in reifen Märkten variiert die Anlagendichte pro 100.000 Einwohner erheblich.{% elif avg_market_score >= 40 %}Ein Score im mittleren Bereich deutet auf eine Wachstumsphase hin: Die Nachfrage ist nachweisbar, die Anlageninfrastruktur baut sich auf, und Preise haben sich noch nicht vollständig auf Wettbewerbsniveau eingependelt. Das eröffnet Chancen für gut positionierte Neueintritte.{% else %}Aufstrebende Märkte bieten First-Mover-Vorteile — weniger direkte Konkurrenz, potenziell attraktivere Mietkonditionen und die Möglichkeit, eine loyale Spielerbasis aufzubauen, bevor sich der Markt verdichtet.{% endif %}
+{% if avg_market_score >= 55 %}Märkte mit Scores über 55 weisen in der Regel eine etablierte Spielerbasis, belastbare Preisdaten und berechenbare Nachfragemuster auf — entscheidend für eine solide Finanzplanung. Dennoch bleiben viele Städte unterversorgt: Selbst in reifen Märkten variiert die Anlagendichte pro 100.000 Einwohner erheblich.{% elif avg_market_score >= 35 %}Ein Score im mittleren Bereich deutet auf eine Wachstumsphase hin: Die Nachfrage ist nachweisbar, die Anlageninfrastruktur baut sich auf, und Preise haben sich noch nicht vollständig auf Wettbewerbsniveau eingependelt. Das eröffnet Chancen für gut positionierte Neueintritte.{% else %}Aufstrebende Märkte bieten First-Mover-Vorteile — weniger direkte Konkurrenz, potenziell attraktivere Mietkonditionen und die Möglichkeit, eine loyale Spielerbasis aufzubauen, bevor sich der Markt verdichtet.{% endif %}
-{% if avg_opportunity_score %}Der durchschnittliche **padelnomics Opportunity Score von {{ avg_opportunity_score }}/100** zeigt, wie viel Investitionspotenzial in {{ country_name_en }} noch unerschlossen ist. {% if avg_opportunity_score >= 60 and avg_market_score < 50 %}Die Kombination aus hohem Opportunity Score und moderatem Market Score macht {{ country_name_en }} besonders interessant: Nachfragepotenzial und Sportaffinität sind vorhanden, die Infrastruktur noch im Aufbau — First-Mover-Konditionen für gut gewählte Standorte.{% elif avg_opportunity_score >= 60 %}Trotz eines bereits aktiven Markts gibt es noch Standorte mit erheblichem Potenzial — vor allem in mittelgroßen Städten und an der Peripherie großer Ballungsräume.{% else %}Viele Standorte in {{ country_name_en }} sind bereits gut versorgt. Neue Projekte brauchen eine sorgfältige Standortanalyse und ein klares Differenzierungsprofil.{% endif %}{% endif %}
+{% if avg_opportunity_score %}Der durchschnittliche **padelnomics Opportunity Score von {{ avg_opportunity_score }}/100** zeigt, wie viel Investitionspotenzial in {{ country_name_en }} noch unerschlossen ist. {% if avg_opportunity_score >= 60 and avg_market_score < 40 %}Die Kombination aus hohem Opportunity Score und moderatem Market Score macht {{ country_name_en }} besonders interessant: Nachfragepotenzial und Sportaffinität sind vorhanden, die Infrastruktur noch im Aufbau — First-Mover-Konditionen für gut gewählte Standorte.{% elif avg_opportunity_score >= 60 %}Trotz eines bereits aktiven Markts gibt es noch Standorte mit erheblichem Potenzial — vor allem in mittelgroßen Städten und an der Peripherie großer Ballungsräume.{% else %}Viele Standorte in {{ country_name_en }} sind bereits gut versorgt. Neue Projekte brauchen eine sorgfältige Standortanalyse und ein klares Differenzierungsprofil.{% endif %}{% endif %}
## Top-Städte in {{ country_name_en }}
@@ -122,7 +122,7 @@ Unsere Spitzenstadt nach
padelnomics Market Score
- {{ avg_market_score }}/100
+ {{ avg_market_score }}/100
{% if avg_opportunity_score %}
@@ -172,15 +172,15 @@ Der **Market Score (Ø {{ avg_market_score }}/100)** bewertet die Marktreife: Be
-{{ country_name_en }} has **{{ total_venues }} padel venues** tracked across **{{ city_count }} cities**. The average padelnomics Market Score across tracked cities is **{{ avg_market_score }}/100**{% if avg_market_score >= 65 %} — a strong market with widespread venue penetration and solid pricing data{% elif avg_market_score >= 40 %} — a growing market with healthy city coverage{% else %} — an emerging market where early entrants can still capture prime locations{% endif %}.
+{{ country_name_en }} has **{{ total_venues }} padel venues** tracked across **{{ city_count }} cities**. The average padelnomics Market Score across tracked cities is **{{ avg_market_score }}/100**{% if avg_market_score >= 55 %} — a strong market with widespread venue penetration and solid pricing data{% elif avg_market_score >= 35 %} — a growing market with healthy city coverage{% else %} — an emerging market where early entrants can still capture prime locations{% endif %}.
## Market Landscape
Padel is growing rapidly across {{ country_name_en }}. Our data tracks {{ total_venues }} venues — a figure that likely understates the true count given independent clubs not listed on booking platforms. The average padelnomics Market Score of {{ avg_market_score }}/100 across {{ city_count }} cities reflects both market maturity and data availability.
-{% if avg_market_score >= 65 %}Markets scoring above 65 typically show an established player base, reliable pricing data, and predictable demand patterns — all critical for sound financial planning. Yet even in mature markets, venue density per 100,000 residents varies significantly between cities, pointing to pockets of underserved demand.{% elif avg_market_score >= 40 %}A mid-range score signals a growth phase: demand is proven, venue infrastructure is building, and pricing hasn't fully settled to competitive levels. This creates opportunities for well-positioned new entrants who can secure good locations before the market matures.{% else %}Emerging markets offer first-mover advantages — less direct competition, potentially better lease terms, and the opportunity to build a loyal player base before the market fills out. The trade-off is less pricing data and more uncertainty in demand projections.{% endif %}
+{% if avg_market_score >= 55 %}Markets scoring above 55 typically show an established player base, reliable pricing data, and predictable demand patterns — all critical for sound financial planning. Yet even in mature markets, venue density per 100,000 residents varies significantly between cities, pointing to pockets of underserved demand.{% elif avg_market_score >= 35 %}A mid-range score signals a growth phase: demand is proven, venue infrastructure is building, and pricing hasn't fully settled to competitive levels. This creates opportunities for well-positioned new entrants who can secure good locations before the market matures.{% else %}Emerging markets offer first-mover advantages — less direct competition, potentially better lease terms, and the opportunity to build a loyal player base before the market fills out. The trade-off is less pricing data and more uncertainty in demand projections.{% endif %}
-{% if avg_opportunity_score %}The average **padelnomics Opportunity Score of {{ avg_opportunity_score }}/100** shows how much investment potential remains untapped in {{ country_name_en }}. {% if avg_opportunity_score >= 60 and avg_market_score < 50 %}The combination of a high Opportunity Score and a moderate Market Score makes {{ country_name_en }} particularly attractive for new entrants: demand potential and sports culture are there, infrastructure is still building — first-mover conditions for well-chosen locations.{% elif avg_opportunity_score >= 60 %}Despite an already active market, locations with significant potential remain — particularly in mid-size cities and at the periphery of major metro areas.{% else %}Many locations in {{ country_name_en }} are already well-served. New projects need careful site selection and a clear differentiation strategy to compete.{% endif %}{% endif %}
+{% if avg_opportunity_score %}The average **padelnomics Opportunity Score of {{ avg_opportunity_score }}/100** shows how much investment potential remains untapped in {{ country_name_en }}. {% if avg_opportunity_score >= 60 and avg_market_score < 40 %}The combination of a high Opportunity Score and a moderate Market Score makes {{ country_name_en }} particularly attractive for new entrants: demand potential and sports culture are there, infrastructure is still building — first-mover conditions for well-chosen locations.{% elif avg_opportunity_score >= 60 %}Despite an already active market, locations with significant potential remain — particularly in mid-size cities and at the periphery of major metro areas.{% else %}Many locations in {{ country_name_en }} are already well-served. New projects need careful site selection and a clear differentiation strategy to compete.{% endif %}{% endif %}
## Top Cities in {{ country_name_en }}
@@ -254,7 +254,7 @@ Our top-ranked city by