If you've ever tried building a sports betting application, odds tracker, arbitrage scanner, value...
Most sports betting projects fail due to data infrastructure complexity—scraping bookmakers, normalizing heterogeneous APIs, and maintaining real-time refresh—rather than prediction model limitations. Success depends on delegating data collection to enable focus on arbitrage detection, alert systems, and trading logic that users actually value.
If you've ever tried building a sports betting application, odds tracker, arbitrage scanner, value betting tool, or sports analytics dashboard, you've probably experienced the same thing:
You start with the exciting part.
The idea.
The algorithm.
The UI.
Most football "tipster" sites have the same trick: they show you the winners and quietly bury the...
The logic behind Polymarket, Kalshi and sports betting apps can be traced back to the inner workings of the slot machine.
I’m building BetLogic, an AI-powered football intelligence platform focused on match previews,...
Why Trading Platforms Are Among the Hardest Software to Build Most software has a generous...
Every quant developer knows the feeling: you write an algorithmic strategy, run it against a basic...
The rise of prediction markets isn't just a financial story, it's a cultural one.
