Validation of the Australian Football League Player Ratings

Abstract

This study investigated the validity of the official Australian Football League Player Ratings system. It also aimed to determine the extent to which the distribution of points across the 13 rating subcategories could explain Australian Football League match outcome. Ratings were obtained for each player from Australian Football League matches played during the 2013–2016 seasons, along with the corresponding match outcome (Win/Loss and score margin). The values for each of the 13 subcategories that comprise the ratings were also obtained for the 2016 season. Total team rating scores were derived as an objective team outcome for each match. Percentage agreement and Pearson correlational analyses revealed that winning teams displayed a higher total team rating in 94.2% of matches and an association of r = 0.96 (95% confidence interval = 0.95–0.96) between match score margin and total team rating differential, respectively. A Partial Decision Tree (PART) analysis resulted in seven rules capable of determining the extent to which relative contributions of rating subcategories explain Win/Loss at an accuracy of 79.3%. These models support the validity of the Australian Football League Player Ratings system and its use as a pertinent system for objective player analyses in the Australian Football League.

Combine performance, draft position and playing position are poor predictors of player career outcomes in the Australian Football League

“…Matches played refers to the number of AFL appearances by the player. The AFL Player Rankings model includes over 100 variables [22] and was popularised by the fantasy competition, Supercoach (www.supercoach.com.au). Based on the principle of field equity, the AFL Player Rating model is determined by the relative effect a player's action increases or decreases their team's expected value of the next score [25].…”

“…This concept of expected value is based on contextual information relating to each possession of the ball (i.e. pressure from opponents, field position, and time of the match) [22]. Draft selection order.…”

“…Consequently, Champion Data (Pty, Ltd) introduced the AFL Player Ranking in 2006, and the AFL Player Rating in 2013. The AFL Player Ranking model is derived from over 100 variables, although is yet to be publicly validated [22]. The AFL Player Rating model is based on the principles of field equity and quantifies a player's actions relative to how they affect their team's likelihood to score next.…”

“…The AFL Player Rating model is based on the principles of field equity and quantifies a player's actions relative to how they affect their team's likelihood to score next. In 94.2% of instances, the team which totalled higher rating points, won the match [22]. Therefore, it is now possible to investigate a player's performance at the AFL draft combine in relation to a player's career performance and thus informs the aims of this research.…”

Sports selection in martial arts based on the harmonic stability of results at competitions

“…The following method, like sports selection according to the latest results in competitions (Elferink-Gemser et al, 2007;Koz et al, 2012;Li, et al, 2014), can be effective in implementing breeding in a relatively short time. However, experimental studies of success in playing hockey (Elferink-Gemser et al, 2007), in football (McIntosh et al, 2018), wrestling (Coetzee et al, 2015;Kajmovic et al, 2014) show significantly significant ambiguity in the results of identifying promising future athletes. Current high scores in competitions are not necessarily indicative of future potential or "talent" (Barreiros et al, 2014).…”