Model Insights
What the model considers important when predicting AFL match outcomes.
Active Model
The current model used to generate all predictions. Tip rate is the percentage of matches where the model correctly picks the winner, measured via walk-forward testing against historical seasons. Margin MAE (Mean Absolute Error) is how far off the predicted margin is on average — lower is better. Features are the individual data points the model considers; only the most useful ones are kept after automated selection.
What Matters Most
Each feature belongs to a category like "Team Form" or "Venue". This shows how much each category contributes to the model's decisions overall. A higher percentage means the model relies more heavily on that type of information when picking winners and predicting margins.
ELO-style offensive and defensive ratings for 10 key stats (scoring, disposals, clearances, contested possessions, etc.), adjusted for opponent quality. A team that scores well against strong defences gets a higher rating than one padding stats against weak opponents.
Recent performance trends — rolling averages of scoring, disposals, clearances, and other stats over the last 3, 5, and 8 games, plus win streaks and consistency measures.
How settled the team is — average shared games between teammates, squad continuity from last week, and how long positional groups (defence, midfield, forward) have played together. More familiarity generally means better team connection.
Underlying team capabilities — offensive efficiency (points per inside 50), defensive solidity (points conceded, intercepts), contested ball, disposal efficiency, and pressure.
The effect of player availability — missing players by position (ruck, inside mid, key forward, key defender), star player absences, role criticality scores, and win impact analysis.
How much the team's selected squad has changed from the previous week — high turnover can disrupt team cohesion.
Situational factors — ladder position, days of rest between matches, whether it's a rivalry derby, and if either team is coming off a bye round.
Coach-related factors — tenure at the club, overall and recent win rates, venue-specific record, new coach bounce effect, and head-to-head coaching matchup history.
Individual player rolling stats — recent averages for disposals, goals, clearances, and tackles for each player, plus form trends.
Ground-specific factors — each team's win rate at the venue, how many games they've played there, and travel distance to the ground.
Log-scaled versions of skewed stats like form shock and momentum — compresses extreme values so a team on a massive streak doesn't dominate the model disproportionately.
How each team historically performs in different conditions — win rates in rain, wind, and heat compared to their overall record, and how their playing style adapts.
Historical matchup data between the two teams — overall H2H win record and how each team's playing style interacts with the opponent's.
Top 20 Most Important Features
The individual data points the model finds most predictive. A feature with high importance has a strong influence on whether the model picks one team over another. For example, if "Home weighted recent margin" ranks highly, it means how well the home team has been winning recently is a strong signal for the model.
| # | Feature | Category | Importance |
|---|---|---|---|
| 1 | Offensive scoring rating adjusted for opponent quality (home-away difference) adjusted_form__diff_off_score_rating | Opponent-Adjusted Ratings | 0.1111 |
| 2 | Defensive scoring rating adjusted for opponent quality (home-away difference) adjusted_form__diff_def_score_rating | Opponent-Adjusted Ratings | 0.0515 |
| 3 | diff team shared win pct cohesion__diff_team_shared_win_pct | Team Cohesion | 0.0237 |
| 4 | Average winning/losing margin weighted toward recent games (home-away difference) team_form__diff_wavg_margin_5 | Team Form | 0.0196 |
| 5 | diff ladder wins contextual__diff_ladder_wins | Context | 0.0176 |
| 6 | diff score launches avg5 advanced_stats__diff_score_launches_avg5 | Advanced Stats | 0.0166 |
| 7 | Away defensive scoring rating adjusted for opponent quality adjusted_form__away_def_score_rating | Opponent-Adjusted Ratings | 0.0162 |
| 8 | Home offensive scoring rating adjusted for opponent quality adjusted_form__home_off_score_rating | Opponent-Adjusted Ratings | 0.0160 |
| 9 | Home points scored divided by points conceded over recent games team_form__home_rolling_pct_5 | Team Form | 0.0152 |
| 10 | Points given up per game — lower is better (home-away difference) team_strengths__diff_defensive_points_conceded_avg | Team Strengths | 0.0138 |
| 11 | Points per game averaged over recent matches (home-away difference) team_form__diff_avg_score_3 | Team Form | 0.0136 |
| 12 | Team's current position on the ladder as a percentage (home-away difference) contextual__diff_ladder_pct | Context | 0.0128 |
| 13 | Away team avg shared wins cohesion__away_team_avg_shared_wins | Team Cohesion | 0.0125 |
| 14 | Forward entries per game (home-away difference) team_form__diff_avg_inside_50s_8 | Team Form | 0.0120 |
| 15 | Home team quality avg disposals player_impact__home_team_quality_avg_disposals | Player Impact | 0.0120 |
| 16 | Coach's overall win rate at this club (home-away difference) coaching__diff_coach_win_rate_recent | Coaching | 0.0119 |
| 17 | Log-scaled version of a skewed stat — compresses extreme values while keeping direction (home-away difference) transform__log_abs_diff_rolling_pct_5 | Transformed Features | 0.0113 |
| 18 | Home average winning/losing margin weighted toward recent games team_form__home_wavg_margin_5 | Team Form | 0.0110 |
| 19 | Composite offensive strength across all stats, adjusted for opponents (home-away difference) adjusted_form__diff_overall_offensive | Opponent-Adjusted Ratings | 0.0105 |
| 20 | Away team quality avg disposals player_impact__away_team_quality_avg_disposals | Player Impact | 0.0101 |
Least Important Features
These features made it through automated selection but contribute the least to predictions. They might be redundant with stronger features, or the pattern they capture just isn't very predictive. For example, a weather feature ranking low means conditions don't meaningfully help the model pick winners in most matches.
| Feature | Category | Importance |
|---|---|---|
Possessions won under direct pressure per game (home-away difference) team_form__diff_wavg_contested_possessions_5 | Team Form | 0.0000 |
How many games the inside midfielders have played together (home-away difference) cohesion__diff_inside_mid_shared_games | Team Cohesion | 0.0000 |
Home how much better or worse the team performs in windy conditions weather_impact__home_weather_windy_win_rate_diff | Weather (Team Performance) | 0.0049 |
Away how much better or worse the team performs in windy conditions weather_impact__away_weather_windy_win_rate_diff | Weather (Team Performance) | 0.0050 |
ground area venue__ground_area | Venue | 0.0050 |
Home points per game averaged over recent matches team_form__home_avg_score_8 | Team Form | 0.0053 |
Away average winning/losing margin weighted toward recent games team_form__away_wavg_margin_5 | Team Form | 0.0054 |
Home weather windy margin diff weather_impact__home_weather_windy_margin_diff | Weather (Team Performance) | 0.0056 |
Forward entry rating adjusted for opponent quality (home-away difference) adjusted_form__diff_off_inside_50s_rating | Opponent-Adjusted Ratings | 0.0056 |
Rebound 50s per game — transitioning from defence to attack (home-away difference) team_strengths__diff_transition_rebound50_avg | Team Strengths | 0.0057 |