Football Predictions & Data-Driven Picks2026-06-18
Daily selections based on a four-algorithm system (Today's Research Selections), combining different modelling approaches to identify more reliable outcomes. Also Blog posts about the models and the experience of using AI coding agents in their building and updating.
StatStrike is a football forecasting app focused on quality over quantity.
Every day, StatStrike monitors leagues worldwide and publishes Over 2.5 and Under 2.5 forecasts from leagues currently meeting performance standards. Rather than flooding users with predictions, the app focuses on fixtures supported by historical league performance and statistical criteria.
Each forecast includes:
Model confidence score
Supporting fixture statistics
Historical league performance metrics
Aggregate market odds (when available)
Transparent track record data
FEATURES
Daily Over 2.5 forecasts
Daily Under 2.5 forecasts
Best Performing category featuring leagues with a proven historical record
Full prediction archive
Automatic result tracking
Performance transparency
Fixture filtering tools
Historical league qualification metrics
TRANSPARENCY FIRST
Every forecast is archived and tracked.
Users can review historical performance, win rates and prediction history directly within the app. No deleted losses. No cherry-picked results. Just measurable forecasting performance.
StatStrike is designed for football fans who value transparency, accountability and data-driven forecasting.
GoalLab forecasts the majority of published global fixtures daily. The algorithm uses an 11 criteria model to forecast Over 2.5 and Under 2.5 football goal bands. It will forecast with all 11 criteria, if available for the fixture, or whatever it can get - forecast confidence is reflected in the volume of criteria available for any given fixture. This doesn't mean a lower confidence forecast is necessarily less accurate than one with more criteria - it depends on the criteria mix and how they interact. Historical win rates of every confidence level is listed as tracked by a rich and growing archive.
Coming soon
PopGoals
A calm bubble-lake app for live hot-zone targets, alerts, and settled win/loss tracking. Not on the App Store yet.
ProphIt Coming Soon!
A new service! Have a theory for predicting goal band outcomes?
This service lets you test your approach using real data, live execution, and transparent tracking — so you can see how it actually performs.
Coming Soon!
PopGoals
Coming soon
iOS app in development. App Store listing and preview copy will follow.
ProphIt — Test Your Own Prediction Ideas in a living app!
Coming Soon!
Have a theory for predicting goal band outcomes?
This service lets you test your approach using real data, live execution, and transparent tracking — so you can see how it actually performs.
How the research service works
You define your ideaDescribe your logic — from simple rules to more detailed concepts.
I build your modelYour idea is translated into a working forecasting algorithm.
We run it liveYour model is executed against real matches over a fixed research period.
You track the resultsYou get access to a dedicated app/dashboard showing:
Predictions
Results (W/L)
Performance over time
What you get
A working version of your idea as a live model
A private dashboard to track performance
Real-world validation (not just backtested theory)
Clear insight into whether your idea has an edge
After the research period
When the initial research period ends, you can:
Extend testing for an additional fee, or
Have your algorithm deployed in a dedicated app for your personal use, for a one-off fixed cost
For ongoing use, you will need a low-cost API subscription for match data. You can connect your own API key, and the system will run your model automatically.
If preferred, managed data access can be provided for a small monthly fee.
Important
This is a research and testing service, not financial advice
No outcomes or profitability are guaranteed
Most ideas do not perform well — that is the purpose of testing
Your model is treated as confidential
Similar outcomes to existing models may occur independently