Baselines first
Well-understood probabilistic families; anything newer must beat them on calibration.
Not who wins. How often “70%” happens.
Sport settles every prediction within hours, so a model that’s confidently wrong has nowhere to hide. We build the modeling and data pipelines that hold up under that kind of scoring.
DataModelCalibration
Where it goes wrong
- A single predicted scoreline, which is close to useless.
- Features that leak the future, so a model looks great offline and random live.
- Accuracy reported instead of calibration.
What we build
Walk-forward features
Each match predicted only from data that existed before it.
Monte Carlo simulation
Model parameters turned into the full outcome space, with uncertainty carried through.
Where AI fits
Machine learning where it beats the baseline, and agents or assistants that explain a forecast to the people using it.
We build modeling and infrastructure. We’re not a betting operation or a tipping service, and nobody honest can sell you a guaranteed edge.
Related services
What do you need to predict honestly?
Tell us the sport, the data and the decision behind it.
Tell us what you want to model