⚙️ How It Works

How AI Football Predictions Work

We analyze football matches as a set of factors and historical patterns. The result is probabilities, not claims about the future.

Entry Point
01

What Does Predizo Actually Predict?

Predizo calculates match outcome probabilities — not certainties. For every fixture the model outputs a probability distribution across three outcomes: home win, draw, and away win.

  • Win / Draw / Loss probability distribution
  • Over/Under goal estimates
  • Model confidence level for each match
Foundation
02

What Football Data Does Predizo Use?

Predizo uses structured, verifiable match data — no insider tips, no rumours, no subjective opinions. Only historical facts that can be independently checked and verified.

  • Past match results across top 5 leagues
  • Team and league statistics
  • Form trends over time
  • Home and away performance patterns
Data is updated regularly as new matches are played.
Intelligence
03

How Does Predizo Transform Raw Data into Match Features?

Raw data is engineered into 76 analytical features that capture the real context of each match. This is where thinking happens — not just feeding a CSV to a model.

  • Team strength ratings
  • Form momentum and dynamics
  • Home vs away behavioral differences
  • Relative opponent advantage metrics
Each match is analyzed in the context of the full season and league.
Core Engine · Phase 2
04

What AI Model Powers Predizo Predictions?

Predizo uses XGBoost v2, trained on 6,564 real matches with 76 features including Dynamic Elo ratings. Post-model adjustments for injuries and fatigue fine-tune every output.

  • XGBoost v2 — 6,564 real matches, 76 features, ~50% accuracy
  • Dynamic Elo — updated after every match (95 teams)
  • H2H history built directly into model features
  • Post-model: injuries (±10%), fatigue (±5%), xG (±3%)
  • Probability cap: 10%–70% per outcome (realistic football range)
Output
05

How Does Predizo Calculate Confidence Scores?

For every match, the system calculates not just probabilities but how certain those probabilities are. This lets you distinguish strong HIGH-confidence signals from uncertain LOW-confidence estimates.

  • Outcome probabilities for every match
  • Model confidence level (Low / Medium / High)
  • Degree of uncertainty in the estimate
Matches with contradictory data are flagged as low confidence.
Feedback Loop
06

How Is the Predizo Model Evaluated and Improved?

After matches are played, every prediction is compared to the actual result. The model is regularly retrained on fresh data and the full prediction history is preserved publicly.

  • Predictions compared to real results
  • Model retrained on fresh data periodically
  • Trained on 6,536 real matches → ~50% accuracy on real data
  • Accuracy statistics tracked and stored
We don't hide prediction history — we use it to improve.

What This Is — and What It Is Not

This is an analytical tool
This is a probabilistic model, not a guarantee of results
This is not advice and not a recommendation

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