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🎲SimulationsπŸ§ͺLab≣Catalog?How this works

sweep: match.late_goal (bool)

← lab Β· AUC 0.384 (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__team.congestion_21d0.0000↑ +0.010no
away__team.elo_momentum_l50.0000β€”no
away__team.form_points_l50.0000β€”no
away__team.goal_diff_avg_l50.0000β€”no
away__team.goals_for_avg_l50.0000β€”no
away__team.goals_against_avg_l50.0000β€”no
away__team.matches_since_blank0.0000↑ +0.221no
away__team.matches_since_clean_sheet0.0000↑ +0.144no
away__team.sot_against_avg_l50.0000↓ -0.155no
away__team.pass_acc_avg_l50.0000↑ +0.173no
away__team.possession_avg_l50.0000↑ +0.115no
away__team.rest_days0.0000↑ +0.032no

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.late_goal (bool)",
  "sport": "football",
  "target": {
    "metric": "match.late_goal"
  },
  "features": "all"
}