Classifying and quantifying team playing styles in the Australian Football League

被引:2
|
作者
Moffatt, Samuel J. [1 ]
Gupta, Ritu [1 ]
Collier, Neil French [2 ]
Keller, Brad S. [3 ]
机构
[1] Curtin Univ, Math & Stat, Perth, Australia
[2] Victoria Univ, Inst Hlth & Sport, Melbourne, Australia
[3] Fremantle Football Club, Perth, Australia
关键词
Playing styles; performance indicator; transactional data; machine learning; clustering; SOCCER;
D O I
10.1080/24748668.2024.2325269
中图分类号
G8 [体育];
学科分类号
04 ; 0403 ;
摘要
Within team sports, match outcomes and events depend on the interaction between thousands of individual actions governed by each team's overriding style of play. Within Australian football, identifying playing styles allows coaches to improve match preparation due to an improved knowledge base that influences decisions involving training sessions, match tactics, and team selection. This study presents the clustering of teams in the Australian Football League (AFL) into playing styles based on match performance indicators extracted from games played between 2013 and 2019. Using k-means clustering, three offensive, two transitional and two scoring styles were identified, combining to create 12 playing styles. The offensive play was grouped into contested, uncontested, and non-distinct styles. The transitional play was clustered into forward half-pressure and defensive half-intercept styles. The scoring play was clustered into stoppage scoring and possession gain scoring styles. The linear model predicting the winning match margin from the explanatory variables of team playing styles and performance indicators had a lower RMSE by 64% compared to models with only playing style explanatory variables and 1.83% to models with only performance indicator explanatory variables. Identifying team playing styles allows coaches to make better-informed decisions regarding match analysis, opposition analysis, and training planning.
引用
收藏
页数:18
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