How Skoor Works

Most football analysis tools give you a percentage and expect you to figure out the rest. Skoor is different. It ingests historical data, team form, head-to-head records, expected goals, defensive metrics, referee tendencies, and dozens more data points — then runs a simulation engine that produces a confidence score for each match.

If the confidence is high, Skoor fires a signal. If the data is unclear, it gives you a pass. That distinction is what separates Skoor from platforms that force a prediction on every match regardless of data quality.

The algorithm doesn't care about narratives or hype. It reads numbers. And it's been tested on 3+ seasons of real match data across 80 leagues before a single signal reaches you.

Every accuracy number you see on Skoor is the result of a walk-forward backtest— the model predicts matches it has never seen using only data available before kickoff. No hindsight, no cherry-picking.

We test across 3+ full seasons of real match data per league. The current production engine has been validated on over 3,900 matches with independent auditor verification.

Our testing doctrine is simple: the algorithm must pass real-world tests at 75%+ accuracy honestly. We do not optimize thresholds to inflate numbers. If accuracy drops, we improve the model — not the test.