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A football resting on a dark wet field under stadium lights at night

The edge was never in having data. It's in the model you build to interpret it.

Hand holding phone with Moddy AI prediction model interface, representing transparent sports betting UX

Bad UX in sports betting isn't just a beginner problem. It fails newer bettors and experienced bettors differently — one through exclusion, one through obscurity. Good design isn't a nice-to-have. It's a philosophical position about who you're actually building for.

Iceberg with small visible tip above water and large hidden mass below, symbolizing hidden sports betting track records

The sports betting picks industry is built on information asymmetry. Opacity isn't a bug — it's the business model. Here's what gets hidden, why it happens, and what the alternative looks like.

Cookie cutter over three sports balls representing flawed universal sports betting models

Sports betting modeling has two main schools: generic cross-sport prediction and market-microstructure strategies. Both exist because outcome-level modeling was team-only work. With 250M data points and a model population, there's a third way.