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So funktioniert es

Wie unsere KI-Vorhersagen funktionieren

A transparent look inside our prediction engine: from raw data to high-confidence picks, powered by six statistical models working in harmony.

01
Schritt 1

Datenerfassung

Our system aggregates data from multiple professional football data providers, ensuring comprehensive coverage and cross-validated accuracy. Data is refreshed multiple times per day.

Spielstatistiken

Goals, shots, possession, corners, cards across 30+ leagues

Spielerdaten

Ratings, injuries, suspensions, and squad availability

Historische Aufzeichnungen

Head-to-head, home/away form, and seasonal trends

Quotenänderungen

Real-time odds from leading bookmakers for value detection

02
Schritt 2

Statistische Modelle

Six specialized models analyze the data from different angles. Each model brings unique strengths to the prediction process.

35%

Poisson-Verteilung

Goal probability modeling

Uses team attack and defense ratings to calculate the probability distribution of goals scored by each team. Generates probabilities for exact scores, over/under, and both teams to score markets.

25%

ELO-Wertung

Team strength comparison

Adapted from chess, our ELO system rates every team based on their match results. Ratings are updated after each game, accounting for opponent strength and home advantage. Higher-rated teams are predicted to win against lower-rated opponents.

20%

Formanalyse

Recent performance trends

Analyzes recent match results to capture momentum and current form. Considers the last 5-10 matches with recency weighting, evaluating goals scored/conceded, clean sheets, and winning/losing streaks.

20%

Value-Bet-Erkennung

Odds vs probability mismatch

Compares our calculated probabilities against bookmaker odds to identify value opportunities. When our model assigns a higher probability than the odds suggest, it signals a potential value bet.

Kor.

Verletzungseinfluss

Squad availability adjustments

Evaluates the impact of injured and suspended players on team strength. Key player absences can significantly alter predictions, especially when star goalkeepers, central defenders, or top scorers are unavailable.

Kor.

Kontextdienst

Derby, referee & motivation factors

Post-ensemble adjustments for factors that pure statistics miss: derby match detection across 19 city groups, referee tendencies for cards and goals, title race and relegation motivation, and league-specific draw calibration.

03
Schritt 3

Ensemble-Vorhersage

Individual model outputs are combined using a weighted voting system. This ensemble approach consistently outperforms any single model because different models excel in different scenarios.

Gewichtete Modellkombination

Poisson-Verteilung 35%
ELO-Wertung 25%
Formanalyse 20%
Value-Bet-Erkennung 20%

Injury Impact and Context Service apply post-ensemble adjustments to fine-tune the final prediction based on squad availability and match context.

04
Schritt 4

Konfidenz & Banko-Auswahl

The final prediction includes a confidence score. Our highest-confidence picks are labeled as "Banko" and must pass three strict criteria.

≥75%

Konfidenzwert

The ensemble model must output at least 75% confidence in the prediction.

≥67%

Modellübereinstimmung

At least two-thirds of prediction models must agree on the outcome.

1.25–1.85

Quotenbereich

Odds must fall within a safe range — not too short, not too risky.

Live-Genauigkeitsverfolgung

Every prediction is settled and tracked. Our accuracy metrics are continuously updated based on real match outcomes.

1X2

Spielergebnis

Win/draw/loss predictions tracked per model

O/U

Über/Unter

Goal total predictions with Brier score analysis

BTTS

Beide Teams treffen

BTTS yes/no accuracy with rolling 30-day average

BS

Brier-Score

Probability calibration metric — lower is better

Bereit für die heutigen Vorhersagen?

Our AI engine generates fresh predictions daily. Explore match forecasts, confidence scores, and Banko picks now.

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