01
Data
Aggregation of data from multiple sources, standardisation, quality control, and model input.
Matchalyse — visuals and materials
Match analysisgrounded in data and models
We run quantitative analysis of sports outcomes: data preparation, statistical and machine-learning models, probability calibration, and validation on historical data. We compare model outputs with years of our own pre-match analysis, including the assessment of betting decisions, so results can be interpreted critically. Estimates are delivered with methodology notes—without claims about a certain match outcome.
Example winning analyses
Selected slips from recent months—different sports and bet types, based on our pre-match analysis. Shown as a record of work, not a guarantee of future results.
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Scroll horizontally — mouse, touch, or keyboard
Who we are
Sport has fascinated us for years—not only as spectacle, but as something that can be measured, studied, and understood more deeply. For years we have analysed matches and events across disciplines on our own, developing our own methods and a view of what really happens in competition. We study the tempo and dynamics of contests, the quality of chances created, player and team form, and contextual factors that simply disappear in standard statistics.
Our model outputs are a starting point—not a final verdict. We run every pre-match analysis systematically, including the context of risk assessment and betting decisions. That experience lets us check whether algorithmic signals align with sporting reality—and catch what no formal model can see on its own.
Method
We use our own application to collect and process data. Four stages—from model input to recommendations grounded in sporting context.
stages
02
Prediction
Feeding processed data into our own machine-learning models, training, and generating predictions.
03
Probability
Generating distributions and probability estimates for key scenarios, with uncertainty intervals where applicable.
04
Recommendation
Interpreting prediction outputs and formulating recommendations with sporting context and our own expertise.
Scope
We develop the product iteratively. We assess model quality with validation and historical tests—we do not publish standalone percentages without sample and methodology context. Conclusions come from a repeatable process, not single fixtures; we broaden league coverage gradually, without promising instant full-market coverage.