Metodología

Cómo se generan los pronósticos de IA en MyScorePrediction: datos de entrada, modelos, puntuación, calibración y límites conocidos.

Data inputs

Each match prediction starts from a structured snapshot built at SSG time:

  • Fixture metadata: kickoff time, venue, home/away assignment, league, round.
  • Recent form for each side (last 5 matches, weighted by recency).
  • Head-to-head record between the two clubs over their last meetings.
  • Home/away split — how each side performs at home versus on the road this season.
  • Probability distributions over win/draw/loss, expected goals, and over/under markets.

Para entender esta métrica, consulta Expected goals.

Models

Predictions are produced by an ensemble of large language models that consume the structured inputs above and output a calibrated probability distribution plus a short reasoning paragraph.

No single model decides a prediction. The pipeline aggregates outputs across models and falls back to the best-calibrated source if a candidate disagrees beyond a configured threshold. Para el modelo probabilístico subyacente, consulta Poisson distribution.

Tournament scoring

When you submit predictions in tournaments your score is computed deterministically:

  • 1 point for a correct outcome (home win / draw / away win).
  • 3 points for the exact final score.

Calibration

Prediction confidence is calibrated against historical results, not against the model's internal certainty. A 60% home-win probability means the model is right roughly 60% of the time on similarly framed matches — not that the home side will win. Para el concepto estadístico, consulta Calibration (statistics).

Known limitations

Football has irreducible variance. The model has no view on injuries, suspensions, or last-minute lineup news unless those signals are present in its training inputs. Predictions are best read alongside current news, not as a replacement for it.

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