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🎲SimulationsπŸ§ͺLab≣Catalog?How this works

sweep: match.total_cards >= 3

← lab Β· AUC 0.563 (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?
home__team.cards_avg_l50.0019↑ +0.075⏳ yes
ratio__team.elo0.0019↓ -0.045⏳ yes
away__team.cards_avg_l50.0016↑ +0.076⏳ yes
market.p_draw0.0015↑ +0.046⏳ yes
match.stakes0.0014↓ -0.029⏳ yes
away__team.congestion_21d0.0014↓ -0.044⏳ yes
x__match.ref_fouls_avg__match.style_clash_corners0.0013↓ -0.003no
x__home__team.goals_for_avg_l5__away__team.defensive_leak_l50.0012↓ -0.020no
match.momentum_clash0.0011↓ -0.006no
home__team.matches_since_blank0.0011↑ +0.000no
market.p_home0.0011↓ -0.044⏳ yes
home__team.dominance_l50.0010↓ -0.002no

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"
}