padelnomics Score
Where the people who would play are: the people in reach, their income and how urban the catchment is, ranked within the country from 0 to 100.
What Is the Padelnomics Score?
The Padelnomics Score answers one question for a place: how much demand could a padel club here draw on, compared with every other place in the same country? It is a rank from 0 to 100. A 79 means the place has more demand within reach than 79% of the country's towns and cities. It is computed for every place with 5,000 people or a padel club already, on the area a club draws players from, about fifteen kilometres around it, and it is recomputed every day. It does not say whether there is room for another club; the section on why the score measures demand alone explains what we tested.
What It Measures
Three parts, all measured on the place's own catchment. The weights and the arithmetic are ours; the three questions are the ones a founder asks about the people a club would serve.
The people in reach
How many people live within a club's reach, on a curve that flattens at around half a million. A club's market is the nearest few hundred thousand people, and the fifth million of a metropolitan area is served by other clubs.
Their income
Income in the catchment, adjusted for purchasing power so that countries compare fairly. Padel is paid by the hour, so a well-off catchment books more of them.
How urban the area is
Whether the catchment's people live close together in a town or are spread across a district. A club fills more easily when its players are a short drive away. We hold no age data, and the score does not pretend to.
From the working to the number
The three parts add up to a demand index from 0 to 100. The score you see is that index's rank within the country: the place with the most demand scores 100, the place with the least scores 0, and every country spreads evenly across the five bands. A German 79 and a Spanish 79 both mean "more demand than 79% of the country", but they are not the same market.
Why the score measures demand alone
Until September 2026 the score also rewarded places with few courts for their population, and places whose clubs ran full. We rebuilt the map as it stood at three past dates and checked where clubs opened next. The supply gap pointed the wrong way: among places of a similar size, clubs opened where courts already were, not where the score saw a gap. A second model that estimated room for more courts did no better than demand alone. So the score now claims only what held up: where the people who would play are.
How to Read the Score
The fifth of the country's places with the least case for another club: courts already at what mature markets reach, or little demand in reach. A new club here competes on quality and price, not on absence.
Some room, mostly taken. Success depends on the exact site and on what the clubs in reach are not offering.
Half the country's places score below this and half above. Worth the Club planner's own circle before a verdict.
Demand in reach, few courts against the benchmark, and where clubs exist they run well. The shortlist starts here.
The top fifth: the most people in reach with the fewest courts among them, and the best-running clubs nearby. The places to look at first.
How each figure on the map is counted
The panel gives you a figure and a bar. This is the working behind each one, in the order the panel asks the question.
Residents
Residents come from population held per map cell, each cell counting for the share of it that falls inside the circle. A cell is about 11 mi across and holds one total for the whole of itself, so a small circle over a city centre understates the people in it.
Clubs and courts
Clubs and courts are the clubs inside the circle that we can vouch for: those a booking platform we read has listed in the last 30 days, and those in a public map or a federation's club list. A club that lists no court count counts at its country's average. The indoor and outdoor split is counted only where a booking platform records it, so it can account for fewer courts than the total. Occupancy is measured from booking availability; court hire is the median advertised rate, taken over the more common currency where the circle spans two.
Room for more courts
Courts this population supports is residents divided by 100,000, times 5. Room for is that figure minus what is already built, and never below zero.
Occupancy, court hire and takings
Monthly takings are each measured club's average priced day over the last thirty, times thirty, summed in the circle's main currency. Clubs whose bookings we cannot read are not estimated.
The clubs already there
The list holds the clubs inside the circle whose booking pages we can read, nearest first. That is a smaller set than the club count above it, and the panel says by how much: a circle can hold clubs we count from a map and cannot measure from a booking system. Ranking the list by a measured figure is part of a subscription; without one the order is distance, which is not a judgement about any club.
What a club here would take
The projection runs the same arithmetic as the financial planner, so moving from the map to the planner cannot produce two answers for one club. You own the three inputs: how many courts, how many hours a day, what an hour costs. The occupancy it assumes is the middle figure of our occupancy band for an indoor club in the country (see how often that band holds, below), or the country's median where we show no band, and the panel says which of the two you are looking at. We do not predict occupancy from price; that would be a demand model, and we do not have one.
How often our occupancy band holds
For a club that does not exist yet, we give a band rather than a single number: the occupancy between which eight in ten clubs with the same roof, indoor or open-air, ran in the same country over the last 30 days. Every day we test it on the clubs whose bookings we read. We take each club in turn, leave out every club in its city, work out the band from the rest, and check whether the club's own occupancy falls inside. A band that does what it says catches about 80% of clubs.
On 2026-10-25, the band held for 79% of the 5,728 clubs we scored. Its middle figure missed a club's own occupancy by 14.4 percentage points on average.
| Country | Clubs | Inside the band | Miss of the middle figure | Band width | Shown |
|---|---|---|---|---|---|
| Spain | 1,087 | 79% | 17.0 points | 54 points | Yes |
| Italy | 1,022 | 80% | 13.8 points | 44 points | Yes |
| United Kingdom | 544 | 79% | 15.6 points | 50 points | Yes |
| Mexico | 455 | 80% | 13.0 points | 41 points | Yes |
| Sweden | 423 | 88% | 11.4 points | 32 points | Yes |
| South Africa | 421 | 78% | 11.7 points | 37 points | Yes |
| Germany | 384 | 79% | 14.9 points | 46 points | Yes |
| Belgium | 177 | 79% | 12.8 points | 38 points | Yes |
| Portugal | 160 | 78% | 14.7 points | 47 points | Yes |
| Switzerland | 128 | 77% | 11.7 points | 41 points | Yes |
| Netherlands | 127 | 78% | 14.6 points | 48 points | Yes |
| UAE | 119 | 68% | 18.5 points | 46 points | No: outside 70–90% |
| United States | 104 | 78% | 14.7 points | 44 points | Yes |
| Finland | 97 | 82% | 13.0 points | 35 points | Yes |
| Denmark | 93 | 77% | 15.3 points | 42 points | Yes |
| Indonesia | 86 | 76% | 15.9 points | 52 points | Yes |
| France | 84 | 75% | 18.3 points | 61 points | Yes |
| Cyprus | 81 | 75% | 15.2 points | 44 points | Yes |
| Norway | 75 | 79% | 12.8 points | 42 points | Yes |
| Romania | 61 | 74% | 11.9 points | 35 points | Yes |
We show a country's band only while it catches between 70% and 90% of its clubs, and only where we read at least 50 clubs. A country outside that range stays in this table with its misses.
What the band does not know: it is the same for every place in a country, because the place itself (residents, courts nearby, income) did not improve it in our backtest. Larger clubs run fuller across a country, but within one city clubs of every size run within about three points of each other, so we leave size out. The band describes the last 30 days, not a whole year, and it comes from clubs that are open and selling. A new club takes time to get there, which is why the planner models a ramp-up.
Our Data
The score combines three categories of input: official statistical data on population, income, and demographics; open geospatial sources for venue locations; and live court booking and pricing data from a curated set of sources. All inputs are refreshed on a regular schedule.
How fresh is the data?
Every metric on this site is recomputed daily. The underlying sources refresh on their natural cadence:
Daily
Live court booking and pricing signals: availability is sampled throughout the day, and occupancy and rate benchmarks are rebuilt every night.
Monthly
Venue locations, court counts and official statistics on population and income.
Quarterly
Building and land-registry data used for hall-level analyses.
Confidence, metric by metric
If we wouldn't put a number in front of a credit committee without a caveat, the caveat is printed here.
| Metric | Based on | Refresh | Confidence | Known limit |
|---|---|---|---|---|
| Venue locations & counts | Open geodata, cross-checked against booking listings | Monthly | High | Very new venues can lag by a few weeks |
| Occupancy (per venue) | Live booking-grid density | Daily | High | Only venues that publish bookable slots online |
| Peak & off-peak rates | Live booking prices | Daily | High | Membership and package pricing is not visible |
| Population & income | Official statistics | Monthly | High | Official releases lag 1–2 years |
| Planner defaults | Blend of the above, capped to plausible planning ranges | Daily | Medium | A starting point: always adjust to your project |
| Padelnomics Score (0–100) | Population and income in reach, ranked within the country | Daily | Directional | Ranks locations; it does not predict revenue |
Limitations
The score evaluates location-level potential, not project-level feasibility. It cannot account for site-specific factors like land availability, zoning, lease costs, or local competition details. Always combine the score with on-the-ground research.
Our coverage of clubs varies by country. National federations report more courts than we can see in some countries: we hold about a third of the courts reported in Spain and Italy, and about a tenth of those in France and Sweden. The score itself is built from population and income, which we hold everywhere, but the court counts shown beside it are lower than the real number in those countries.
Occupancy is a booking-grid proxy
We measure booked density on venues that publish bookable slots online. Walk-ins, club-internal bookings and venues without online booking are invisible to it. In markets with dense online-booking coverage the signal is strong; elsewhere it is thinner and treated as such.
Defaults are starting points, not verdicts
Planner defaults are blended market medians, capped to plausible planning ranges. Your venue, your lease and your pricing will differ. That is why every assumption is adjustable and the sensitivity analysis exists.
The score ranks; it doesn't predict
A high score means strong fundamentals relative to other locations, not a revenue promise. Treat it as a screening tool that gets you to a shortlist, not a decision.
Source dependency, stated openly
Our occupancy benchmarks lean on live booking data from a curated set of sources. If access to one source ever changed, the fallback is defined in advance: additional booking platforms and venue-level sampling. Benchmarking would degrade gracefully, not vanish.
Found a number that looks wrong? Tell us at [email protected]. We check every report.
Frequently Asked Questions
What is the Padelnomics Score?
A rank from 0 to 100 within a country of the demand a padel club could draw on: the people within reach, their income and how urban the area is. A 79 means the place has more demand in reach than 79% of the country's places.
How often is the score updated?
Every metric is recomputed daily. Booking and pricing signals are sampled throughout the day; venue locations and official statistics refresh monthly; building data quarterly.
Why does a city with many padel courts still score high?
Because the score measures demand, not a gap. A large, well-off city scores high whether it has many courts or few. When we tested a score that marked such cities down for their courts, it pointed away from where new clubs actually opened. To judge the competition in a particular area, look at the clubs and courts on the map around it.
Does the score tell me there is room for another club?
No. It tells you where the demand is. Whether a particular area has room for your club depends on the clubs already there, how full they run and what you would build, which is what the Club planner projects for a site you pick.
Can I use the score for my business plan?
Yes, as a screening tool for site selection. Use it to shortlist places with strong demand, then use the Club planner and the financial planner to model the revenue, costs and returns of your own project at a specific site.