Multifactorial Benchmarking of Longitudinal Player Performance in the Australian Football League

Abstract

This study aimed to develop a model to objectively benchmark professional Australian Rules football (AF) player performance based on age, experience, positional role and both draft type and round in the Australian Football League (AFL). The secondary aims were to identify the stage of peak performance and specific breakpoints in AF player performance longitudinally. AFL Player Ratings data were obtained for all players ( n = 1052) from the 1034 matches played during the 2013–2017 seasons, along with data pertaining to the abovementioned player characteristics. Two separate linear mixed models revealed that all factors influenced player performance, with age and experience the strongest in each model, respectively. Post hoc Tukey tests indicated that performance was affected by age at each level up until the age of 21 (effect ranging from 0.98 to 3.70 rating points), and by experience at the levels 1–20 and 21–40 matches in comparison to all higher levels of experience (effect ranging from 1.01 to 3.77 rating points). Two segmented models indicated that a point of marginal gains exists within longitudinal performance progression between the age levels 22 and 23, and the experience levels 41–60 and 61–80 matches. Professional sporting organisations may apply the methods provided here to support decisions regarding player recruitment and development.

Factors Affecting Physical and Technical Performance in Australian Football

“…This potentially highlights a “sweet spot”, where players may be in their prime 3–6 years into their careers. McIntosh et al (31) identified the age of 22, or 60 matches, as a time where playing performance is at its peak within AF. This varies slightly with the moderate experience group within this study, whose ages ranged between 21 and 25 years, with average matches completed 51.…”

Apples and oranges? Comparing player performances between the Australian Football League and second-tier leagues

“…These could be used as a more suitable measure of seasonal player performance than mean player rankings, and could similarly be used to support organisational decisions regarding recruitment and list management. This type of ranking is generalisable to all players in the dataset for each respective phase, as the random effects account for the fixed effects used in our models, allowing for comparisons between players across different ages, leagues and playing positions (McIntosh, Kovalchik, & Robertson, 2019). For example, the comparison of Figures 5A and 5B outline how the mean season CD ranking points and player random effects in Phase One could be used to visualise seasonal performances for players across various seasons.…”

An investigation of data-driven player positional roles within the Australian Football League Women's competition using technical skill match-play data

“…Australian Football (AF) can be described as a 'dynamic invasion team sport'. 1 The national competition for women's AF, the Australian Football League Women's (AFLW) competition, was established in 2017. Research on the AFLW competition has included characterising physical [2][3][4] and technical match-play performance.…”