Technical Skill Testing Predicts Status in Junior ...

Technical Skill Testing Predicts Status in Junior Australian Football Carl T.E. Woods1, Annette J. Raynor1, Lyndell Bruce2 and Zane McDonald1 1School of Exercise and Health Sciences, Edith Cowan University, Joondalup, Western Australia, Australia; 2School of Medical Sciences, Royal Melbourne Institute of Technology University, Melbourne, Australia;

Introduction Introduction Talent identification (TID) in the Australian Football League (AFL) is often the combination of subjective and objective examinations stemming from junior Australian Football (AF) playing competitions and an annual draft combine.1 However, subjectively assessing skill proficiency within junior AF playing competitions may be unreliable, whilst the tests used within the National draft combine predominantly assess physical capacity and fail to assess other critical components such as technical proficiency. Additionally, forecasting prospective playing potential on physical parameters could be misleading, as they do not fully encapsulate playing ability in AF.2,3 Consequently, it is currently unknown if sports specific technical skill is predictive of selection onto an elite junior AF team. If so, it may highlight the importance of technical skill testing within test batteries aimed at identifying talent in junior AF.

Methods Subjects Players were recruited from the 2013 under 18 (U18) West Australian Football League (WAFL) competition and classified into two groups; elite (state U18 representatives; n=25; 17.9 ± 0.5 y) and subelite (non state U18 selection; n=25; 17.3 ± 0.6 y). The Edith Cowan University Human Research Ethics Committee provided ethical approval with all players and parents/guardians (if players were under 18 years of age) providing informed consent prior to testing. Procedures Players completed a kicking test referred to as the Australian Football Kicking (AFK) test, with this being adapted from that used within the AFL national draft combine.5 Data was collected on an outdoor grassed football (AF) field on multiple occasions, thus a wind speed and temperature variation of less than 5 km/hr and 5oC, respectively, was ensured upon each testing occasion.

Figure 1. Diagrammatic representation of the AFK test (right side only shown)

Start Cone: Turn Cone:

4m

Release Line:

2m 14.4 m

The Scorer: Radar Gun:

20 m 20 m

The AFK Test The psychometric properties of the test were evaluated prior to use in this research. Players were required to nominate their dominant and non dominant leg prior to completing this test, with dominance defined as their preferred kicking leg. One kick was completed at each distance, with the first three kicks being on their dominant leg. For example, if their dominant leg was their left leg, they would kick to the targets on the right of their body. When cued by a whistle blown by the scorer, the player ran to the turn cone, made a 1800 degree turn (self-directed) and disposed the ball from behind the release line to a pre-specified target player; positioned within a target circle (Figure 1). The target player was randomly called by the scorer before each disposal by the scorer. Each target was only nominated once per side. Kicking accuracy was assessed using the point allocation seen in Table 1, with total score on dominant and non dominant legs being the two accuracy criterion variables. A Stalker-radar gun was used to assess ball speed, with average peak ball speed on each side being used as the two speed criterions.

Purpose

30 m

4m 40 m 35 m

Table 1. Scoring criteria for the AFK test

The purpose of this study was to identify if technical skill (specifically kicking accuracy and ball speed) was predictive of status (elite/subelite) in junior (under 18) AF. It should be noted that the results presented have been taken from a broader research program, which included additional physical, technical and tactical assessments.

Statistical Analysis A MANOVA was used to test the main effect of ‘status’ (2 levels: elite, sub-elite) on the four criterion variables, with the effect size (ES) of status was calculated using Cohen’s d statistic. Logistic regression models were built using the four criterion variables as predictors, whilst a bootstrapped receiver operator curve (ROC) was produced to assess the discriminant ability of the best predictive model; with an area under the curve (AUC) of 1 representing perfect discriminant power.

Point(s)

Criteria

3

Ball reached the target player on the full and they did not have to leave the target circle to receive possession

2

Ball reached the target player on the full but they placed one foot out of the target circle to receive possession.

1

Ball reached the target player on the full but they placed both feet out of the target circle to receive possession.

0

Target player did not receive possession of the ball on the full.

Results According to the Pillai’s Trace (V), the MANOVA revealed a significant effect of status on the criterion variables (V = 0.623, F(7, 42.000) = 9.930, p = 0.000), with the follow-up univariate analysis revealing a significant effect for dominant and non dominant side accuracy and dominant and non dominant side ball speed. (Table 2).

From a possible seven regression equations that had a delta of less than 10, it was the combination of accuracy and ball speed on the dominant and non dominant sides that was the best predictor of status (wi = 0.25; delta = 0.00) (Table 3). Consequently, a combined score of 127.3 (each predictor summated), successfully classified 76% and 84% of the sub-elite and elite players respectively (AUC = 89.4%) (Figure 2). Interestingly, the greatest reduced model retained accuracy on the dominant side and ball speed on the non dominant side as the best single term predictors of status (Table 3).

Table 2. Between group effects (Mean ± SD)

Table 3. Model summary table showing the ranking of each model based on Akaike’s weights (wi)

Measurement

LL

df

AIC

delta

wi

~ DomAcc + DomBS + NonDomAcc + NonDomBS

-16.83

5.00

45.02

0.00

0.25

0.86

~ DomAcc + DomBS + NonDomAcc

-18.09

4.00

45.06

0.04

0.25

57.3 ± 4.2

1.01

~ DomAcc + NonDomBS

-19.78

3.00

46.08

1.06

0.15

52.7 ± 4.6

1.03

Null (~ 1)

-34.66

1.00

71.40

26.38