sweep: match.rained (bool)
β lab Β· AUC 0.499 (no real pattern β honest result) 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.
| Factor | Importance | Direction | Survives all eras? |
|---|
| away__t3__team.season_ppg | 0.0072 | β +0.016 | no |
| diff__team.shots_for_avg_l5 | 0.0068 | β +0.018 | no |
| match.elo_gap | 0.0064 | β +0.018 | no |
| away__team.season_ppg | 0.0062 | β +0.011 | no |
| home__t3__team.shots_for_avg_l5 | 0.0060 | β +0.001 | no |
| ratio__team.corners_against_avg_l5 | 0.0055 | β -0.001 | no |
| ratio__team.corners_volatility_l10 | 0.0051 | β -0.006 | no |
| home__t3__team.corners_against_avg_l5 | 0.0049 | β -0.006 | no |
| diff__team.dominance_l5 | 0.0049 | β +0.017 | no |
| home__team.matches_since_clean_sheet | 0.0046 | β -0.012 | no |
| home__team.possession_avg_l5 | 0.0046 | β +0.018 | no |
| home__t3__team.h2_goals_l10 | 0.0045 | β -0.005 | 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 Β· the reproducible recipe
{
"name": "sweep: match.rained (bool)",
"sport": "football",
"target": {
"metric": "match.rained"
},
"features": "all"
}