Global Feature Importance
Which features drive the ensemble model's predictions?
Top 20 Features
All Features
| Rank | Feature | Importance | Relative |
|---|---|---|---|
| 1 | proj_over_under | 0.2287 |
|
| 2 | proj_wins | 0.1414 |
|
| 3 | proj_win_pct | 0.1056 |
|
| 4 | pythag_wins_prev_year | 0.0639 |
|
| 5 | schedule_strength | 0.0384 |
|
| 6 | road_win_pct_prev_year | 0.0344 |
|
| 7 | whip_prev_year | 0.0281 |
|
| 8 | runs_allowed_prev_year | 0.0279 |
|
| 9 | park_adj_runs_allowed_prev_year | 0.0266 |
|
| 10 | park_adj_home_runs_prev_year | 0.0235 |
|
| 11 | home_win_pct_prev_year | 0.0201 |
|
| 12 | over_under_prev_year | 0.0186 |
|
| 13 | total_war_prev_year | 0.0183 |
|
| 14 | bullpen_war_prev_year | 0.0173 |
|
| 15 | rotation_war_prev_year | 0.0157 |
|
| 16 | wins_prev_year | 0.0142 |
|
| 17 | age_diff_prev_year | 0.0140 |
|
| 18 | home_road_diff_prev_year | 0.0139 |
|
| 19 | win_pct_prev_year | 0.0137 |
|
| 20 | wins_3yr_avg | 0.0133 |
|
| 21 | pythag_luck_3yr_avg | 0.0132 |
|
| 22 | era_prev_year | 0.0131 |
|
| 23 | home_runs_prev_year | 0.0123 |
|
| 24 | avg_age_batting_prev_year | 0.0122 |
|
| 25 | runs_scored_prev_year | 0.0118 |
|
| 26 | batting_avg_prev_year | 0.0117 |
|
| 27 | ops_prev_year | 0.0116 |
|
| 28 | bat_war_prev_year | 0.0098 |
|
| 29 | avg_age_pitching_prev_year | 0.0094 |
|
| 30 | pit_war_prev_year | 0.0089 |
|
| 31 | park_adj_runs_scored_prev_year | 0.0083 |
|
| 32 | ops_proj | 0.0000 |
|
| 33 | era_proj | 0.0000 |
|
| 34 | runs_scored_proj | 0.0000 |
|
| 35 | whip_proj | 0.0000 |
|
| 36 | pythag_wins_proj | 0.0000 |
|
| 37 | win_pct_proj | 0.0000 |
|
| 38 | over_under_proj | 0.0000 |
|
| 39 | manager_win_pct | 0.0000 |
|
| 40 | manager_tenure_years | 0.0000 |
|
| 41 | injured_war | 0.0000 |
|
| 42 | injured_pitcher_war | 0.0000 |
|
| 43 | injured_batter_war | 0.0000 |
|
| 44 | war_added | 0.0000 |
|
| 45 | war_lost | 0.0000 |
|
| 46 | net_war_change | 0.0000 |
|
Feature Categories
Prior Performance
- • Previous season wins
- • 2-year rolling average
- • 3-year rolling average
- • Win % trends
Projections
- • ZiPS projected wins
- • Steamer projected wins
- • Depth Charts consensus
- • FanGraphs WAR projections
Contextual
- • Park factors
- • Division strength
- • Year-over-year change
- • Playoff status
Ensemble Composition
The predictor uses a weighted ensemble of three complementary models:
- XGBoost (50%): Captures non-linear interactions between features (e.g., park factors × lineup strength)
- Random Forest (30%): Robust to outliers and provides uncertainty estimates via tree variance
- Ridge Regression (20%): Linear baseline for interpretability and stability
Feature importance shown above is averaged across all three models, weighted by their ensemble contribution.