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🎲SimulationsπŸ§ͺLab≣Catalog?How this works

sweep: match.total_cards >= 3

← lab Β· AUC 0.561 (weak signal) Β· ran 7/4/2026

What this is: Asks which pre-match factors drive one specific outcome, using a walk-forward model and permutation importance.
FactorImportanceDirectionSurvives all eras?
away__team.defensive_leak_l50.0009↓ -0.023⏳ yes
market.p_home0.0007↓ -0.044⏳ yes
market.disagreement_home0.0007↑ +0.005no
diff__team.cards_avg_l50.0007↓ -0.000no
away__team.travel_km0.0006↓ -0.029no
away__t3__team.h2_goals_l100.0005↓ -0.006no
home__team.sot_for_avg_l50.0005↓ -0.005no
ratio__team.conversion_l50.0004↓ -0.003no
x__home__team.elo_momentum_l5__away__team.elo_momentum_l50.0004↓ -0.007no
away__t3__team.matches_since_clean_sheet0.0004↓ -0.002no
match.mismatch0.0004↓ -0.029⏳ yes
home__t3__team.matches_since_clean_sheet0.0004↑ +0.005no

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.total_cards >= 3",
  "sport": "football",
  "target": {
    "op": ">=",
    "value": 3,
    "metric": "match.total_cards"
  },
  "features": "all"
}