Model Insights

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.

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.

A cumulative team strength rating built from all historical results. Teams gain ELO for wins and lose it for losses, with bigger shifts for upsets. The difference between two teams' ELO ratings predicts the expected winner.

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

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

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.

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.

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.

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.

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.

Historical matchup data between the two teams — overall H2H win record and how each team's playing style interacts with the opponent's.

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

Travel and rest factors — days between games, cumulative travel distance, interstate trips, timezone shifts, consecutive away games, bye timing, and travel direction changes.