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.

xgboost
Model Type
70%
Tip Rate
16.8
Margin MAE
100/100
Features Selected

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.

Opponent-Adjusted Ratings27.7%

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.

Team Form15.2%

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.

Advanced Stats9.1%
Team Cohesion8.7%

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.

Team Strengths6.1%

Underlying team capabilities — offensive efficiency (points per inside 50), defensive solidity (points conceded, intercepts), contested ball, disposal efficiency, and pressure.

Player Impact6%

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.

Roster Change5.7%

How much the team's selected squad has changed from the previous week — high turnover can disrupt team cohesion.

Context5.3%

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.

Coaching4%

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.

Player Form3.4%

Individual player rolling stats — recent averages for disposals, goals, clearances, and tackles for each player, plus form trends.

Venue3%

Ground-specific factors — each team's win rate at the venue, how many games they've played there, and travel distance to the ground.

Transformed Features2.8%

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.

Weather (Team Performance)1.6%

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.

Player Rating0.7%
Head-to-Head0.6%

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.

#FeatureCategoryImportance
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.

FeatureCategoryImportance
Possessions won under direct pressure per game (home-away difference)
team_form__diff_wavg_contested_possessions_5
Team Form0.0000
How many games the inside midfielders have played together (home-away difference)
cohesion__diff_inside_mid_shared_games
Team Cohesion0.0000
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
Venue0.0050
Home points per game averaged over recent matches
team_form__home_avg_score_8
Team Form0.0053
Away average winning/losing margin weighted toward recent games
team_form__away_wavg_margin_5
Team Form0.0054
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 Ratings0.0056
Rebound 50s per game — transitioning from defence to attack (home-away difference)
team_strengths__diff_transition_rebound50_avg
Team Strengths0.0057
Model version: v20261106-0745-xgboost