Hello. We're André and João, co-founders of Matchlytics.
This is the first thing we've written on our own site. So it feels right to start with why we built it, and what we're trying to do.
Matchlytics is padel intelligence. One camera per court, capturing every match from the serve to the last shot off the glass, and turning it into something clubs, coaches, and players actually use.
Clubs enhance their facility with it. Coaches build sessions around it. Players see their own game for the first time.
An extra app is always an extra app
Before writing a single line of code, we agreed on one thing.
It doesn't matter how good the technology is. If we ask a player to download something new, scan a QR code, create another account, or change a habit they didn't know they had — we've already lost a part of them.
This was the core principle behind our integration with TieSports, the booking platform most Portuguese clubs already run on. We didn't want to be a standalone tool you remember to open. We wanted to be a natural part of something players already do.
Book a court. Play. Open the app. Your match is there — recorded, edited, analysed.
No extra steps. No friction. No drop-off.
The ecosystem matters more than any single piece
But removing friction was only the starting point.
What we're really building is an ecosystem.
Camera hardware, AI models, booking platforms, player profiles, club management, performance data — none of it matters in isolation. The value lives in the connections between them. A match isn't a video; it's the rally, the coach's note, the opponent's pattern, the club's next event, the player's next session. All of it, linked.
We're early. Most of what we're excited about isn't shipped yet. But every integration we ship is one more thread in that fabric.
What you'll find here
This is where we'll write about what we're building, how it works, and what the data is showing us.
Changelog entries for every release. Long-form posts when we have something worth saying — a data story from a tournament match, an engineering deep-dive into how a model was trained, a note on why we made a product decision the way we did.
See you in the next one.
— André & João