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🎲SimulationsπŸ§ͺLab≣Catalog?How this works

sweep: match.comeback_happened (bool)

← lab Β· AUC 0.494 (no real pattern β€” honest result) Β· ran 7/5/2026

What this is: Asks which pre-match factors drive one specific outcome, using a walk-forward model and permutation importance.
FactorImportanceDirectionSurvives all eras?
away__t3__team.elo_momentum_l50.0001↑ +0.003no
away__team.blown_lead_rate_l200.0000↑ +0.003no
away__team.comeback_rate_l200.0000↓ -0.002no
away__team.cards_avg_l50.0000↑ +0.012no
away__team.conversion_l50.0000↓ -0.016no
away__team.corner_diff_avg_l50.0000↑ +0.007no
away__team.corners_against_avg_l50.0000↓ -0.008no
away__team.congestion_21d0.0000↑ +0.008no
away__team.corners_volatility_l100.0000↑ +0.004no
away__team.defensive_leak_l50.0000↑ +0.004no
away__team.dominance_l50.0000↑ +0.013no
away__team.elo0.0000↑ +0.009no

Reading the columnswhat each number actually means

AUCpredictability: 0.50 = coin flip, ~0.70 = ceiling for sports
Importancehow much the model leans on this factor (permutation importance)
Directionsign of the raw correlation with the outcome
Survives all eraseffect points the same way in every historical era
Spec Β· the reproducible recipe
{
  "name": "sweep: match.comeback_happened (bool)",
  "sport": "football",
  "target": {
    "metric": "match.comeback_happened"
  },
  "features": "all"
}