When ChatGPT names a padel club, that mention did not come from nowhere. It came from one of two mechanisms, and each one can be worked on deliberately. Understanding the split is the difference between hoping to be recommended and engineering it.
The two ways ChatGPT knows about you
The first mechanism is training data. The model has read a huge snapshot of the public web, and if your club appeared clearly and repeatedly in that snapshot, the model may know you exist, where you are, and what you offer. You cannot edit training data after the fact, but you can influence the next snapshot: models are retrained, and the clubs that publish clear, consistent information now are the ones baked into future versions.
The second mechanism matters more day to day: live search. For local questions like “where can I play padel near me tonight”, ChatGPT usually browses, pulling fresh results through its search index and reading the pages it finds. This is where most padel club citations actually come from, and it behaves much more like search visibility than like magic. If your pages rank for the underlying query, load fast, and state the facts plainly, the assistant can find you, read you, and name you.
What the assistant needs to read
Once your page is retrieved, the assistant has seconds of compute to extract facts. Make extraction trivial.
State the essentials as text on your homepage and a dedicated facts page: full address, opening hours, number of courts, indoor or outdoor, prices, and how to book. Every one of those facts should survive copy and paste. If it lives in an image, a slider, or a PDF, it does not exist for the machine.
Add structured data. A club marked up with the right schema hands the assistant a machine-readable card instead of forcing it to guess. Answer real questions in text: a visible FAQ that covers beginners, equipment rental, coaching, and walk-in play matches the exact phrasing people use in prompts.
Keep your name consistent everywhere. ChatGPT cross-checks what it reads against maps listings, booking platforms, and directories. When your website, your Google profile, and your booking app all agree, you look like one solid entity. When they disagree, you look uncertain, and assistants do not recommend uncertainty.
Being where the assistant looks
ChatGPT’s browsing leans on a conventional search index, which means the sources that rank for padel queries in your city are the sources it reads. Get into them. The city sports guides, the local news piece about padel’s growth, the federation club directory, the league tables: each one is a page the assistant may retrieve, and each one that names your club is a vote for you.
This is why the competitive picture is local. The clubs you are competing against for a citation are the ones in your own market, and in most markets almost none of them have done this work. Our clubs page breaks down the common failure patterns. City by city the dynamics shift: in Barcelona the fight is to stand out among dozens of legible venues, while in Prague a single well-structured club can own the entire answer.
How to check whether it is working
Do not guess. Ask ChatGPT the questions your customers ask, in a fresh chat, and record what comes back. Ask for clubs in your area, for beginner-friendly venues, for courts available in your district. Note who gets named and what the assistant says about them. Then run your site through the free AI Readiness Scorecard, which checks the structural signals this article describes: readable facts, structured data, crawler access, and machine-readable pages.
Repeat the same questions monthly. Citations move slowly at first, then compound: once one strong source names you, others corroborate, and the assistant’s confidence in you rises across every related query.
There is no trick and no shortcut, which is the good news. The clubs ChatGPT cites are the clubs that made themselves easy to cite. That work is finite, it is mostly done once, and right now, in almost every city, nobody else in your market has done it.