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By Marcell Balogh — CTO, Lead Data Scientist & ML Engineer at Dinkmate.ai At Dinkmate, we live by one rule: build everything in house. Not for the fun of reinventing wheels, but because owning the full stack is the only way to move fast without depending on someone else's dataset, licensing terms, or roadmap. This post covers one piece of that philosophy: how we generate synthetic training data for pickleball court and ball detection, entirely from scratch. The problem with r

By Marcell Balogh, CTO / Lead Data Scientist & ML Engineer, Dinkmate.ai AI is rewriting the rulebook for sports technology. Building a computer vision or ML pipeline that tracks a player, scores a rally, or reads a swing is easier today than it's ever been. Open-source models, pretrained weights, and off-the-shelf frameworks have collapsed months of engineering work into days. But that ease of development hides a trap: teams start believing the model is the product. It isn't.

At Dinkmate, we’ve always believed that ratings should reflect more than just wins and losses. Ratings should capture how you really play...

Pickleball is growing faster than ever, with over 30 million players across the U.S.—yet the majority still don’t have an official...

Sándor’s connection to pickleball runs deeper than business—it’s about building a thriving community. A passionate player himself, Sándor...

Marcell’s journey into pickleball is just beginning, but he’s picking up the game remarkably fast. With his background in interactive...