Background: Australian football (AF) players require endurance, strength, speed, and agility to be successful. Tests assessing physical characteristics are commonly used for talent identification; however, their ability to differentiate between players across the Australian Football League's (AFL) participation pathway remains unclear. The objective of this review was to quantify the physical characteristics of male AF players across the AFL participation pathway. Methods: A search of databases was undertaken. Studies examining tests of physical performance were included, with 27 meeting the inclusion/exclusion criteria. Study appraisal was conducted using a checklist of selection criteria. Results: The 20-m sprint time was the most reported test, followed by vertical jump (VJ), AFL planned agility, and 20-m multi-stage fitness test (MSFT). The fastest times for 20-m sprint were for Elite AFL players (range 2.94-3.13 s), with local-level players the slowest (3.22-4.06 s). State Junior Under (U) 18s (58-66 cm) had higher jumps than senior players, with the lowest jumps reported for Local U10s (mean 31 cm). No elite-level data were reported for the AFL planned agility or 20-m MSFT. AFL planned agility times were only reported for talent pathway levels, with large performance variability evident across all levels (8.17-9.12 s). Only mean 20-m MSFT scores were reported from Local U10s to National Draft Camp (6.10-13.50 shuttles). Conclusions: Talent pathway players exhibit similar mean test scores irrespective of the physical test, with the exception of 20-m sprint and VJ. Physical tests can discriminate between local participation level players but are less useful within the AFL talent pathway.

Selection bias exists towards older players is present at the AFL's State U16, and is maintained at State and National level combines. Age-policy changes are only partially successful at addressing the RAE at the National level, with alternative strategies also recommended in order to address the RAE across the AFL talent pathways.

Physical testing-based draft combines are undertaken across various sporting codes to inform talent selection. To determine the explanatory power of the Australian football league (AFL) draft combine, participants drafted between 1999-2016 (n = 1488) were assessed. Testing performance, draft selection order and playing position, AFL matches played, AFL player ranking points and AFL player rating points were collected as career outcomes. Boosted regression tree analysis revealed that position and draft selection order were the most explanatory variables of career outcomes. Linear modelling based on testing results is able to explain 4% of matches played and 3% of in-game performance measures. Each individual combine test explained <2% of the matches played outcome. Draft selection order demonstrated mixed results for career outcomes relative to playing position. For instance, key forwards and draft selection order were observed as a slight negative relationship using the AFL Player Ranking points career outcome measure. These findings indicate that the AFL draft combine is a poor measure for informing talent selection, thus providing minimal utility for the practices investigated in this study.

Three-dimensional motion capture systems such as Vicon have been used to validate commercial electronic performance and tracking systems. However, three-dimensional motion capture cannot be used for large capture areas such as a full football pitch due to the need for many fragile cameras to be placed around the capture volume and a lack of suitable depth of field of those cameras. There is a need, therefore, for a hybrid testing solution for commercial electronic performance and tracking systems using highly precise three-dimensional motion capture in a small test area and a computer vision system in other areas to test for full-pitch coverage by the commercial systems. This study aimed to establish the validity of VisionKit computer vision system against three-dimensional motion capture in a stadium environment. Ten participants undertook a series of football-specific movement tasks, including a circuit, small-sided games and a 20 m sprint. There was strong agreement between VisionKit and three-dimensional motion capture across each activity undertaken. The root mean square difference for speed was 0.04 m·s−1 and for position was 0.18 m. VisionKit had strong agreement with the criterion three-dimensional motion capture system three-dimensional motion capture for football-related movements tested in stadium environments. VisionKit can thus be used to establish the concurrent validity of other electronic performance and tracking systems in circumstances where three-dimensional motion capture cannot be used.

This study investigated the utility of physical fitness and movement ability tests to differentiate and classify players into Australian Football League (AFL) participation pathway levels. Players (n = 293, age 10.9-19.1 years) completed the following tests; 5-m, 10-m and 20-m sprint, AFL planned agility, vertical jump (VJ), running vertical jump, 20-m Multi-Stage Fitness Test (MSFT), and Athletic Ability Assessment (AAA). A multivariate analysis of variance between AFL participation pathway levels was conducted, and a classification tree determined the extent to which players could be allocated to relevant levels. The magnitude of differences between physical fitness and movement ability were level dependent, with the largest standardized effects (ES) between Local U12, Local U14s, and older levels for most physical fitness tests (ES:-4.64 to 5.02), except the 5-m and 10-m sprint. The 20-m, 5-m, AFL agility, 20-m MSFT, overhead squat, and running VJ (right) contributed to the classification model, with 57% overall accuracy reported (43% under cross-validation). National U16 players were easiest to classify (87%), while National U18 were most difficult (0%). Physical fitness tests do not appear to differentiate between players following selection into AFL talent pathway levels. Other attributes (i.e., skill, psychological, and socio-cultural) should be prioritized over physical fitness and movement attributes by selectors/coaches when considering selection of talented players.

word count: 248 Text-only word count: 3,117 ABSTRACT Purpose: To establish levels of association between physical fitness and match activity profiles of players within the Australian Football League (AFL) participation pathway. Methods: Players (n = 287, range 10.9 -19.1 years) were assessed on 20-m sprint, AFL agility, vertical jump (VJ) and running VJ, 20-m multi stage fitness test (MSFT), and Athletic Abilities Assessment (AAA). Match activity profiles were obtained from global positioning system (GPS) measures; relative speed, maximal velocity, and relative high speed running (HSR). Results: Correlational analyses revealed moderate relationships between sprint (r = 0.32-0.57, p ≤ 0.05), and jump test scores (r = 0.34-0.78, p ≤ 0.05) and match activity profiles in Local U12, Local U14, National U16 and National U18s, except jump tests in National U18s. AFL agility was also moderate-to-strongly associated in Local U12, Local U14, Local U18, and National U16s (r = 0.37-0.87, p ≤ 0.05), and strongly associated with relative speed in Local U18s (r = 0.84, p ≤ 0.05). Match relative speed and HSR were moderate-to-strongly associated with 20-m multi-stage fitness test (MSFT) in Local U14, Local U18, and National U18s (r = 0.41-0.95, p ≤ 0.05), and AAA in Local U12, and Local U18s (p = 0.35-0.67, p ≤ 0.05). Match activity profile demands increased between Local U12 and National U16s then plateaued. Conclusions: Physical fitness relates more strongly to match activity profiles in younger adolescent and National level players. Recruiters should consider adolescent physical fitness and match activity profiles as dynamic across the AFL participation pathway.

The aim of this study was to explore differences in the physical fitness and anthropometric profiles between birth year quartiles of players attending the Australian Football League (AFL) National Draft Combine. Date of birth, anthropometric, 20 m sprint, vertical and running vertical jump, AFL planned agility, and 20 m Multi-Stage Fitness Test (MSFT) data were obtained for players selected to attend the Combine between 1999 and 2019 (n = 1549; Mage = 18.1; SDage = 0.3). The underlying density distributions of the data were visually explored using violin plots overlaid with box and whisker plots. A multivariate analysis of variance (MANOVA) was then used to model the main effect of birth quartile (four levels) on the physical and anthropometric scores. Results showed that physical and anthropometric test scores did not significantly differ according to birth quartile (V = 0.008, F = 0.880, p = 0.631). We conclude that the physical and anthropometric profiles of high-level junior Australian Football players were similar according to birth year quartile across the modeled period. Therefore, how players utilize their physical and anthropometric attributes during game-play via contextualized, representative assessments, such as small-sided games, should be considered when examining potential causes of a RAE.

19This study investigated whether adding a maximal voluntary isometric contraction