The efficacy of talent selection criteria in the Australian Football League

Abstract

This study investigated the association between talent selection criteria, draft order and match performance in professional Australian Football players. Physical performance results from the Australian Football League (AFL) National Draft combine and non-performance based talent selection criteria were collated for all players drafted in the National Draft with selections 1-80 between 2003 and 2008 (n = 318). Match performance was assessed via the AFL Player Ranking metric that was provided by a commercial statistical provider (Champion Data Pty Ltd). A combination of stepwise multiple regression and linear mixed model analyses examined the influence of National Draft combine physical performance assessments and non-performance based talent selection criteria on draft order and future match performance. Earlier draft selection was associated with Under-18 all Australian team selection, height, Under-18 National Championship participation and indigenous status. The 20 m sprint and state of origin were associated with later draft selection. Under-18 all Australian team selection increased both Player Ranking/game and total Player Ranking. Under-18 all Australian team selection displays efficacy for selecting players with the potential for success.

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

“…In Phase Two, the comparisons highlight that there are substantial differences between the four senior second-tier leagues as well as the under-18 national championships, in comparison to each of the reserve and under-18 state leagues. With the under-18 national championships being a main draft pathway for elite under-18 players (Sullivan, et al, 2020), and the majority of players competing in these championships also competing in one or more of these reserve and under-18 state leagues, these findings reiterate the need for a method to quantify the difference in objective player performance measurements between leagues.…”

“…Each of the second-tier leagues and their specific inclusion within the study are presented in Table 1. The CD ranking points were utilised as the objective measure of player performance in this study due to its availability across each of the eleven leagues/competitions, as well as its previous use in the AF notational literature (Gogos, Larkin, Haycraft, Collier, & Robertson, 2020;Hiscock, Dawson, Phillips, Mitchell, & Barake, 2016;Sullivan, Kempton, Ward, & Coutts, 2020). The CD ranking points are produced by statistics provider CD (Champion Data Pty Ltd., Melbourne, Australia), and measure a player's performance by awarding players a fixed value for specific performance actions.…”

Physical Fitness and Performance in Talented & Untalented Young Chinese Soccer Players

“…Sport-specific technical skills assessments have been demonstrated a great sensitivity to discriminate different competitive levels and predict future sports performance in the talent identification area [ 42 ]. The present multidimensional proposal is based on a MLR analysis and has been previously applied to predict or explain sports performance, both in individual sports, such as swimming [ 43 ], triathlons [ 44 ], or artistic gymnastics [ 45 ], and in team sports, such as field hockey [ 46 ], handball [ 17 , 47 ], volleyball [ 48 ], or soccer [ 49 , 50 , 51 ]. However, its practical application in team sports is more complex, due to the necessary configuration of a ranking which acts as a response or dependent variable in MLR analysis, and which in many individual sports is already established by its own rules and regulations (e.g., ATP ranking in tennis, FINA points in swimming, ITRA points in trail running, etc.).…”