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Shots at goal in Australian Football: Historical trends, determinants of accuracy and common strategies
by
Young, Christopher M.
, Dwyer, Dan B.
in
Accuracy
/ Arc angle
/ Athletic Performance
/ Australia
/ Australian football
/ Competitive Behavior
/ Expected goal
/ Humans
/ Optimisation
/ Soccer
/ Tactics
/ Teams
/ Trends
2024
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Shots at goal in Australian Football: Historical trends, determinants of accuracy and common strategies
by
Young, Christopher M.
, Dwyer, Dan B.
in
Accuracy
/ Arc angle
/ Athletic Performance
/ Australia
/ Australian football
/ Competitive Behavior
/ Expected goal
/ Humans
/ Optimisation
/ Soccer
/ Tactics
/ Teams
/ Trends
2024
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Do you wish to request the book?
Shots at goal in Australian Football: Historical trends, determinants of accuracy and common strategies
by
Young, Christopher M.
, Dwyer, Dan B.
in
Accuracy
/ Arc angle
/ Athletic Performance
/ Australia
/ Australian football
/ Competitive Behavior
/ Expected goal
/ Humans
/ Optimisation
/ Soccer
/ Tactics
/ Teams
/ Trends
2024
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Shots at goal in Australian Football: Historical trends, determinants of accuracy and common strategies
Journal Article
Shots at goal in Australian Football: Historical trends, determinants of accuracy and common strategies
2024
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Overview
To understand the historical context of and relationships between, the characteristics of shots at goal and match outcome in the Australian Football League.
Observational.
Descriptive statistics of shots at goal were calculated and compared across 20 seasons. The location, type, and outcome of all shots at goal (n = 43,254), by all teams (2017–19 & 21), were compared with match outcome.
The total number of shots per match and the accuracy of shots haven't changed in two decades. Most teams win by having more shots at goal (Wilcoxon-r effect size 0.63) than their opponent (i.e. “majority strategy”) and of these, the number of open shots (0.48) is slightly more important than set shots (0.43), followed by shot accuracy (0.29). However, some teams (14 %) win by taking fewer shots at goal from field locations with a higher likelihood of scoring a goal (i.e. “minority strategy”). Arc angle and shot type can be used to predict the outcome of a shot at goal with 60.3 % classification accuracy. The novel shot-outcome prediction model reported here provides a better opportunity to evaluate goal kicking performance of teams and players, because it accounts for the type and difficulty of the shot.
Teams can evaluate the shot accuracy of their players more fairly, by accounting for shot location using the method reported here. Coaches can compare the two shot strategies identified and implement the one that suits the skill profile of their players and increase their likelihood of winning.
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