sweep: match.second_half_goals >= 1

← lab · AUC 0.518 (no real pattern — honest result) · ran 7/4/2026

What this is: Asks which pre-match factors drive one specific outcome, using a walk-forward model and permutation importance.

Factor	Importance	Direction	Survives all eras?
home__team.corners_for_avg_l5	0.0004	↑ +0.019	no
ratio__team.goals_volatility_l10	0.0002	↑ +0.009	no
x__away__team.goals_for_avg_l5__home__team.defensive_leak_l5	0.0002	↑ +0.003	no
home__team.elo	0.0002	↑ +0.034	⏳ yes
home__t3__team.matches_since_blank	0.0001	↓ -0.009	no
ratio__team.cards_avg_l5	0.0001	↓ -0.005	no
ratio__team.conversion_l5	0.0001	↑ +0.003	no
away__team.comeback_rate_l20	0.0001	↑ +0.018	no
ratio__team.venue_ppg_l5	0.0001	↑ +0.011	no
away__t3__team.season_ppg	0.0001	↑ +0.003	no
diff__team.shot_diff_avg_l5	0.0001	↑ +0.015	no
diff__team.corners_against_avg_l5	0.0001	↓ -0.009	no

Reading the columnswhat each number actually means

AUC	predictability: 0.50 = coin flip, ~0.70 = ceiling for sports
Importance	how much the model leans on this factor (permutation importance)
Direction	sign of the raw correlation with the outcome
Survives all eras	effect points the same way in every historical era

Spec · the reproducible recipe

{
  "name": "sweep: match.second_half_goals >= 1",
  "sport": "football",
  "target": {
    "op": ">=",
    "value": 1,
    "metric": "match.second_half_goals"
  },
  "features": "all"
}