Every prediction on Scorina starts with the same question a good analyst asks: how many goals is each team likely to create and concede in this specific match? We answer it with data rather than opinion — recent results, scoring and conceding rates, expected goals, the venue, and the history between the two sides. Those signals are combined by a machine-learning model trained on thousands of past matches, which returns the probabilities you see on the app.
None of it is hand-typed. When a fixture appears, the engine gathers the numbers, builds the features, runs the model, and produces the prediction automatically. Below is that journey, step by step.
The prediction pipeline
/ data to predictionCollect data
Form, goals, expected goals, standings and head-to-head for both teams.
Build features
Attack & defence strength, venue and form-weighted expected goals.
Run the model
A trained model converts the features into match probabilities.
Prediction
Win chances, expected goals and the likeliest scoreline.
The data behind every match
/ four signalsA prediction is only as good as what goes into it. For each fixture, Scorina pulls together four kinds of information about both sides.
Results & form
Recent matches for each team — wins, draws and losses, and the goals they scored and conceded.
Expected goals
How many goals each side should score and concede from the quality of chances, not just the score.
League standings
Season-long attack and defence strength — a stable baseline for how good each team really is.
Head-to-head
Recent history between the two teams — some matchups favour one side regardless of form.
Expected goals: the core
/ how xG is blendedThe heart of a Scorina prediction is a projected xG for each team — our estimate of the goals they'll create. We blend several views of a team's attack and defence, each weighted by how telling it is: season form is steady but slow, recent form and the venue capture what's happening now, and head-to-head nudges it for the opponent.
Two corrections keep it honest: a strength multiplier for the gap in quality between the sides, and an xG regression step that pulls over- or under-performing teams back toward what they actually deserve.
From goals to a prediction
/ probabilities & scorelineWith a projected xG for each team, a machine-learning model trained on thousands of matches turns those features into three numbers that add to 100% — home win, draw, or away win — and estimates the likeliest scoreline. The relationship between expected goals and results isn't a straight line, which is where the model earns its keep.
What drives a single prediction
/ key factorsNo two matches lean on the same factors. For a given fixture, some signals matter more — a big xG gap, a fortress home record, a lopsided head-to-head. Scorina also surfaces the key factors behind each call in plain language, so you see the reasons, not just the number.
Honest about the limits
/ what a model can't seeA model is a guide, not a crystal ball. Scorina predicts from the data it has — it doesn't know about a late injury, a rested squad before a cup tie, or the weather. Football is famously unpredictable, and upsets are part of the game. Our numbers tell you what's likely, framed as probabilities rather than certainties, and they're built for analysis and entertainment — not betting advice.
Open the analysis page and run a prediction for any fixture. Open Scorina →
Predictions are statistical estimates for analysis and entertainment only, not betting advice. Figures update continuously as new data arrives.