  Australian Rules Football is a field invasion game where two teams attempt to
score the highest points to win. Complex machine learning algorithms have been
developed to predict match outcomes post-game, but their lack of
interpretability hampers an understanding of the factors that affect a team's
performance. Using data from the male competition of the Australian Football
League, seasons 2015 to 2023, we estimate team strengths and the factors
impacting them by fitting flexible Bradley-Terry models. We successfully
identify teams significantly stronger or weaker than the average, with stronger
teams placing higher in the previous seasons' ladder and leading the activity
in the Forward 50 zone, goal shots and scoring over their opponents. Playing at
home is confirmed to create an advantage regardless of team strengths. The
ability of the model to predict game results in advance is tested, with models
accounting for team-specific, time-variant features predicting up to 71.5% of
outcomes. Therefore, our approach can provide an interpretable understanding of
team strengths and competitive game predictions, making it optimal for
data-driven strategies and training.
