Abstract
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
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Article info
Publication history
Published online: May 11, 2022
Accepted:
May 10,
2022
Received in revised form:
May 6,
2022
Received:
December 12,
2021
Identification
Copyright
© 2022 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.
