Objectives: Australian football goal kicking is vital to team success, but its study is limited. Develop and apply Bayesian models incorporating temporal, spatial and situational variables to predict shot outcomes. The models aim to (i) rank players on their goal kicking and (ii) create clusters of statistically similar players and rank these clusters to provide generalised recommendations about player types.
Design: Retrospective longitudinal study with goal kicking data from three seasons, 2018-2020, 576 official Australian Football League matches, containing 26,818 attempts at goal from 778 players.
Methods: The Bayesian ordinal regression model enables descriptive analysis of goal kicking performance. The models include spatial variables of distance and kick angle, situational variables of shot type and player or cluster with interaction terms. Alternative models included situational variables of weather and player characteristics, spatial variables of stadium location and temporal variables of time and quarter. Approximate leave-one-out cross validation was used to test the model.
Results: Overall goal rate of 47% (12,600), behind rate of 35% (9373) with misses the remaining 18% (4845). Accuracy of both player and cluster model achieved 0.51 against an uninformed (predict goal) model result of 0.47. The models allow for analysis of goal kicking accuracy by distance and angle and analysis of player and player-type performance.
Conclusions: While credible intervals for all players for set shots and general play were relatively large, some 95% credible intervals excluded zero. Therefore, it may be concluded that some players' goal kicking skill can be quantified and differentiated from other players.
Keywords: Bayesian ordinal regression; Football; Mixed effects models; Performance indicators.
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