⚽ Tomulator
Overview
β—ˆDashboardπŸ“…Today's gamesβ—ŽState of play
Money
πŸ’·Bets & strategies
Explore
⊞Leagues & seasonsπŸ“Metricsβ–€Data sources
Engine room
🎲SimulationsπŸ§ͺLab≣Catalog?How this works

sweep: match.temp_c >= 10.7

← lab Β· AUC 0.783 (real 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?
match.stakes0.0691↑ +0.035⏳ yes
away__t3__team.season_ppg0.0119↑ +0.009no
away__t3__team.congestion_21d0.0079↓ -0.034⏳ yes
away__team.rest_days0.0078↑ +0.152⏳ yes
away__team.season_ppg0.0061↑ +0.004no
away__t3__team.h2_goals_l100.0056↑ +0.010no
home__t3__team.possession_avg_l50.0056↑ +0.006no
home__team.rest_days0.0053↑ +0.156⏳ yes
home__team.season_ppg0.0047↑ +0.002no
home__team.sot_against_avg_l50.0041↑ +0.006no
diff__team.goals_volatility_l100.0035↑ +0.019no
diff__team.cards_avg_l50.0034↓ -0.004no

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.temp_c >= 10.7",
  "sport": "football",
  "target": {
    "op": ">=",
    "value": 10.7,
    "metric": "match.temp_c"
  },
  "features": "all"
}