An analysis of factors impacting team strengths in the Australian Football League using time-variant Bradley-Terry models

TL;DR

This paper employs time-variant Bradley-Terry models to analyze factors influencing team strengths in Australian Football League, providing interpretable insights and predicting match outcomes with up to 71.5% accuracy.

Contribution

It introduces flexible, interpretable Bradley-Terry models to estimate team strengths and key performance factors over multiple seasons in AFL.

Findings

Stronger teams tend to rank higher and lead in key game zones.

Home advantage significantly impacts game outcomes.

Models predict up to 71.5% of match results.

Abstract

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.