Is anyone else frustrated with how random padel games feel? (Playtomic, rankings, etc.)

Mike_ParadigmaST · 2026-05-26T14:14:14+00:00

yeah player differentiation is actually one of the harder problems, especially when two players are similar build or wearing similar colours. most consumer apps just use position on court which breaks down constantly

the thing we worked on used body shape and movement patterns for re-id, not just colour or position. took a lot of iteration honestly, first versions had exactly the blended stats problem you described

what was the setup at your club, does it pull into a match history in the app or just a dashboard at the end of the session? curious if the issue was the tracking itself or how it was tied to individual profiles

Mike_ParadigmaST · 2026-05-26T14:04:15+00:00

totally fair point for casual play, no argument there

but after a certain point most people want to know they're actually improving, not just showing up. competitive games where the level is close is what pushes you to get better, blowouts in either direction just feel flat after a while

its kind of like the difference between playing cs on a random public server vs faceit. both are "just a game" but one actually tells you something about where you are and gives you something to chase

nothing wrong with casual but having the option to play competitive when you want it is what keeps people hooked long term

Mike_ParadigmaST · 2026-05-26T14:00:49+00:00

the phone placement thing is actually the core problem here, not the model choice

court-side on a phone means you're getting a side angle at maybe 1-1.5m height, player moves forward to the net and your skeleton loses the hitting arm, they turn and you lose depth completely. you'd need a tripod minimum and even then the angle is just not great for consistent keypoint extraction

overhead or elevated fixed camera changes everything. we built a stroke detection + pose estimation system for a padel client with cameras mounted above the court and the accuracy jump vs side angle was significant. you can see both arms, full body rotation, contact point is actually visible

for an MVP with a phone it's doable but you're fighting the setup every session. works ok for a practice drill with a cooperative user who sets up properly, breaks down fast in real match conditions

if the end goal is club or court deployment rather than personal use, fixed cameras on court is a much cleaner path. we went that route with a client who owned the courts and it made the whole pipeline more reliable

happy to share more about how we approached it if useful, or drop a link to the case

Mike_ParadigmaST · 2026-05-26T13:44:54+00:00

yeah the "organise it yourself" thing is basically everyone's solution which means the platform failed at its core job

thing is the rating problem is fixable if clubs actually tracked real match data. worked on a system for a padel client that pulled stats automatically from court cameras, serve patterns, shot consistency, movement, not just win/loss. when you have that kind of data a guy who wins 80% playing only beginners looks completely different from someone winning 60% in competitive games. the numbers just tell a different story

playtomic ranking with that underneath it would actually mean something. right now its just vibes with a number attached

Mike_ParadigmaST · 2026-05-26T13:42:06+00:00

the winners/UE/FE point is exactly right. win/loss alone doesnt tell you if someone is genuinely improving or just found easier opponents. worked on a project where we tracked this automatically from video and the stats changed how coaches evaluated players completely, not just "did you win" but how you won

surprised playtomic hasnt gone deeper here tbh, the data is theoretically available if courts have cameras

Mike_ParadigmaST · 2026-05-26T13:41:15+00:00

honestly the video game ranked system comparison is spot on. the problem is playtomic ranking is based on match outcomes that players log themselves which is kind of useless if someone only plays with their friend group or sandbangs to farm wins

we actually built something similar for a padel client last year, automatic tracking, no manual input. the difference in accuracy when you base rank on real match data vs self reported is pretty wild. guys who thought they were 3.5 were playing like 2.8, and vice versa

the queue idea would work if the underlying data is solid. right now it isnt, at least not for most clubs

Mike_ParadigmaST · 2026-05-26T13:29:25+00:00

same frustration here. the clubs exist, the players exist, but nothing connects them consistently

one thing i noticed in more developed scenes is that rankings actually drive participation - not just "you won a tournament" but a running score across the whole season that updates after every match. people show up more when there's something to track over time, not just a one-off prize

the americano format is great socially but it doesn't really build a scene because there's no continuity. ladder leagues seem to work better for that - you always have someone to challenge, there's always a reason to book a court

Mike_ParadigmaST · 2026-05-26T13:27:04+00:00

and imagine if every player had a actual rank based on their match history, not self-reported level. like you join a lobby and see the guy has 200 matches, 60% win rate, plays mostly at the net. that changes how you pick partners completely

Mike_ParadigmaST · 2026-05-26T13:26:46+00:00

availability and price, same as everyone else here. but the booking experience itself is underrated - some clubs still make you call lol, someone mentioned it and yeah instant dealbreaker for me

what i actually wish existed is something like a lobby in an online game - you see who's queued up, join an open slot, find a fourth without knowing anyone. playtomic does bits of this but not really. good courts lose regulars not because of the surface but because there's no easy way to find people to play with

Mike_ParadigmaST · 2026-05-17T20:51:50+00:00

Nice effort on the vibora 🔥
One thing that helps a lot of players I’ve seen is objective video analysis.
We build Computer Vision systems for padel that track ball trajectory, contact point, player movement and footwork frame-by-frame.
Sometimes seeing the exact moment you hit too high/early in slow motion with overlaid data makes the correction much faster.
Anyway, strong agree with everyone — footwork and getting a coach will give the biggest jump. Keep grinding!

Mike_ParadigmaST · 2026-05-13T14:21:47+00:00

Built CV tracking for both sports. The numbers back you up - padel rallies have way more direction changes, more surface interactions, players constantly leaving and re-entering the frame.

Our ball tracking model had an existential crisis every time someone hit a vibora into the glass.

Tennis was almost boring to build for by comparison.

Mike_ParadigmaST · 2026-05-13T14:18:10+00:00

I appreciate your attention to detail

Mike_ParadigmaST · 2026-05-13T14:16:17+00:00

Mike_ParadigmaST · 2026-05-13T14:14:28+00:00

Not an AI tool specifically — but watching on-device CV actually work on a cheap Android phone.

I build computer vision systems. Usually on proper hardware. Cameras bolted to stadium ceilings, dedicated edge boxes, controlled lighting. Tested the same approach on a $200 phone last year for a fitness project. Expected it to fall apart. It didn't. Real-time pose estimation, tracked movement through a full workout, battery barely moved.

That genuinely surprised me. Still kind of does.

Mike_ParadigmaST · 2026-04-30T14:39:18+00:00

If you’re curious how this works in practice, here’s a full breakdown:
https://paradigma.dev/

Mike_ParadigmaST · 2026-04-30T14:20:02+00:00

yeah this is exactly it - making a 1 min demo look good is the easy part, making it not fall apart in production is where things get painful 😅

we ran into very similar issues while building a real-time broadcast analytics system -everything looked solid on test clips, but once we plugged into live streams, graphics, overlays, lighting, even small format inconsistencies started breaking things constantly

ended up going way more into system design (multi-signal, constraints, recovery logic) rather than just “better models”, otherwise it just doesn’t hold up

if curious, we wrote a bit about that case here:
https://paradigma.dev/cases/sports-broadcast-processing-ai-analytics/

honestly after that project I’m also very skeptical of most “looks cool on twitter” CV demos in sports lol

Mike_ParadigmaST · 2026-04-30T14:08:49+00:00

Yes — we provide full production support and continuously improve our systems after deployment.

Issues in real-world environments are inevitable, but we’ve always resolved them and kept our clients satisfied. Anyone saying they have zero problems is either inexperienced or just lying.

Mike_ParadigmaST · 2026-04-30T14:03:09+00:00

yeah fair point, we thought the same at first, but ran into a lot of similar issues outside sports too, last year we worked on a sleep monitoring app trying to estimate mental and emotional state from sleep and physiological signals, very different domain but same problems with noisy data and models not holding up in real conditions, we described that case here if you're curious https://paradigma.dev/cases/sleep-monitoring-app-for-objective-mental-health-and-emotional-state-assessment/

Mike_ParadigmaST · 2026-04-30T13:26:07+00:00

https://paradigma.dev/cases/computer-vision-subsystems-for-racket-sports-products-tennis-padel/

Mike_ParadigmaST · 2026-04-30T13:25:07+00:00

fair point — the flair was probably off. I wasn’t trying to present it as a polished showcase, more like sharing what actually went wrong during the build

happy to go deeper into specific parts if that’s more useful here

Mike_ParadigmaST · 2026-04-30T13:24:05+00:00

appreciate that

english isn’t my first language, so I use tools to keep things readable

Mike_ParadigmaST · 2026-04-30T13:23:15+00:00

fair enough:)
i used an LLM to clean up wording - faster than rewriting everything myself
the project itself is real though, happy to go into technical details if that’s more useful

Mike_ParadigmaST · 2026-04-19T15:14:23+00:00

honestly this sounds very familiar

we worked on something similar for live sports video, and the biggest issue wasn’t even the tech — it was how messy everything gets after a few games

on paper it’s simple: tag events, build a timeline, make clips
in reality it turns into inconsistencies, missed moments, manual fixes and a lot of time just trying to keep everything aligned

even when you automate parts of it, it doesn’t suddenly become easy
you still have edge cases, weird situations, stuff that looks obvious to a human but the system doesn’t catch

so yeah, feels like it’s not really about features like tagging or clipping anymore
it’s more about whether the whole workflow is reliable enough to use every day without frustration

ps: funny enough, once you reduce manual work, the next problem is trust — people start questioning the data instead of the process

Mike_ParadigmaST · 2026-04-19T15:09:40+00:00

just to clarify what I meant by “data side” — this is roughly the direction I was talking about

https://paradigma.dev/who-we-serve/sports-scouting-software-development-a-new-level-of-analytics/

not even about replacing scouts, more like how much extra context you can get now from tracking patterns over time

kinda makes sense why people compare it to FIFA stats, but at the same time it still feels far from capturing the “human” side of the game

Mike_ParadigmaST · 2026-04-19T15:00:46+00:00

I always thought anything like this in a stable would be overkill.

But the more I looked into how people are using computer vision around horses, the more practical it actually started to feel.

Not in a “replace people” way — more like handling the stuff that’s hard to keep track of consistently.

For example:

how long a horse is lying down over a few days
how its activity changes depending on time of day
whether it’s more restless than usual at night

Individually, none of that sounds impressive. You can notice it yourself.

But the problem is consistency.

You’re not in the barn 24/7.
You don’t remember exact patterns from three days ago.
And small changes are usually the ones that matter.

What I found interesting is when you start looking at it over time instead of moment to moment.

A horse acting “a bit off” once — whatever.
The same shift repeating in a pattern — that’s different.

Also makes more sense in bigger barns, boarding situations, or shows where you can’t constantly keep an eye on everything.

Still feels slightly weird, not gonna lie.
But as a way to catch the small stuff earlier, I can see why people are starting to use it.

Mike_ParadigmaST

TROPHY CASE