Given the financial and temporal constraints associated with the acquisition of sporting expertise, strategies that streamline athlete skill learning may be of value to governing sporting bodies (1). As such, the identification of talented junior athletes who demonstrate considerable performance potential is becoming an increasingly prominent practice for national sporting bodies, federations, and clubs (21,26). This talent identification process affords practitioners with the opportunity to tailor training strategies to a select few juniors; referred to as talent development (TDE; defined as the process of exposing talent-identified juniors to a learning environment that intends to expedite the acquisition of expertise) (29). Many national sporting organizations around the world have established elite TDE academies, with examples including ASPIRE in Qatar, and the United Kingdom High Performance Talent Program. Following suit, the Australian Football League (AFL) annually invests substantial resources into the identification and development of talented junior Australian football (AF) players believed to possess the qualities enabling their effective participation within the AFL (5).
These elite TDE programs in junior AF, referred to as State Academies, consist of 2 critical transition stages; the first occurs at under 16 years (U16) level, and the second occurs at the under 18 (U18) level. Thus, the elite junior talent pathway initiates at the U16 level in AF. The primary goal of these academies is to minimize performance discrepancies between elite junior and senior competition levels by exposing talent-identified juniors to an opportunistic learning environment through the provision of superior coaching, player welfare, and sport science services (32). Consequently, it is understood that the current training interventions employed within these academies are based on perceived performance differences between the junior and senior levels.
To date, research in AF has primarily focused on the identification of the performance differences between the elite U18 level and the AFL (elite senior level). Whilst these studies have demonstrated that measures of anthropometry (28), game-speed (5), athletic movement skill (30), and physical fitness characteristics (2,7) are discriminative of the elite junior (U18) to senior (AFL) level, no studies have investigated performance differences within the junior talent pathway. Specifically, there is yet to be work that identifies performance differences between the U16 and U18 levels within a State Academy. This is an important gap to address, as identifying the performance qualities most discriminative of junior developmental level may create a strong basis for the establishment of targeted training interventions at the initial stage of the AF talent pathway (e.g., the U16 level), which may ultimately assist with the junior to senior developmental transition.
The benefit of identifying performance differences between juniors at different stages of a talent pathway has been shown in other team invasions sports, namely rugby league (24) and soccer (27). Vaeyens et al. (27) demonstrated that measures of running speed and technical skill were more discriminant of talent in soccer at the U13 and U14 levels, while measures of cardiorespiratory endurance were more discriminant of talent at the U15 and U16 levels. From these results, it was suggested that practitioners develop tailored training interventions for each developmental level with the aim of minimizing the performance gaps between these junior levels (27).
Across each developmental level in AF, players require a unique combination of physical (e.g., repeat sprint ability and maximal aerobic capacity), technical (e.g., kicking and handballing), and perceptual (e.g., offensive and defensive decision-making) skills to enable a successful performance (12,31,32). Given this, recent talent identification models in AF have progressed toward the use of multidimensional designs to assist with the recognition of superior holistic performance qualities (32). Similar multidimensional work is required to assist with the development of talent-identified juniors within the AF talent pathway. To progress toward this multidimensionality in talent development, work is required to examine developmental differences (e.g., U16 and U18) with regards to each component required for a successful performance (e.g., physical, technical, and perceptual). Acknowledging this, the aim of this study was to provide an initial basis for the development of a multidimensional model of TDE in AF by identifying the physical fitness, anthropometric and athletic movement qualities discriminant of developmental level in the elite junior talent pathway. Given their longitudinal exposure to an opportunistic learning environment, it was hypothesized that the U18 players would possess superior performance qualities relative to their U16 counterparts. The subsequent findings of this work are likely to assist with the design of targeted training interventions purported to assist with TDE in junior AF, which may ultimately enhance the overall elite junior to senior transition for talent-identified juniors.
Experimental Approach to the Problem
To test the study hypothesis, an observational cross-sectional research design was used. All players participating in this study performed a test battery that consisted of physical fitness, anthropometric and athletic movement skill assessments. The construction of this test battery was based on recommendations provided in the literature (10,19,20,31). Testing took place at the end of the player's respective preseason phase of training in an attempt to standardize testing conditions.
Data were collected from a total sample of 77 talent-identified junior AF players who all originated from the same State Academy. From this total sample, 2 player groups were defined based on their developmental level; U16 (n = 40, 15.6–15.9 years) and U18 (n = 37, 17.1–17.9 years). All players selected into the State Academy participated in the study. Ethical approval was granted by the relevant Human Research Ethics Committee before data collection and written parental consent was obtained for players.
Players completed the following test battery at an indoor location in 1 testing session on the same day; standing height, body mass, 20-m sprint test, the AFL agility test, repeat sprint test, a stationary vertical jump (SVJ) test, a dynamic vertical jump (DVJ) test (performed on both dominant [D] and nondominant [ND] foot takeoff), the 20-m multistage fitness test, and an athletic movement skill assessment (16). All players completed a standardized warm-up before the physical fitness tests, which consisted of light jogging, unilateral and bilateral countermovement jumps, and dynamic stretches, taking approximately 15 minutes to complete. The anthropometric measurements of standing height and body mass were the first measurements obtained. Players were required to remove their footwear before commencing the anthropometric assessments, and measurements were recorded to the nearest 0.1 cm and 0.1 kg, respectively.
The physical fitness assessments were completed in a circuit fashion with the players being randomly allocated to a starting point in smaller groups consisting of 6–8 players. The following order was applied to the circuit: (a) 20-m sprint test, (b) AFL agility test, (c) SVJ test, (d) DVJ dominant (DVJD)–foot test, (e) DVJ nondominant (DVJND)–foot test, and (f) repeat sprint test. For tests consisting of multiple trials, 1 minute was allocated between trials, and 2 minutes was allocated between the conclusion and initiation of each testing station. Verbal encouragement was provided for each physical test requiring maximal effort. After the physical fitness tests, players performed the athletic movement skill assessment. This assessment consisted of 6 movements as follows: (a) overhead squat, (b) single-leg Romanian deadlift right leg, (c) single-leg Romanian deadlift left leg, (d) double lunge right leg, (e) double lunge left leg, and (f) push-up. Finally, all players undertook the 20-m multistage fitness test after the completion of all other testing; being split into approximately 2 equal-sized groups within their respective developmental level to perform this test. Although the measurement protocols for assessment are provided in greater detail elsewhere (30,31), a brief procedural description is provided below.
Stationary and Dynamic Vertical Jump Height
Jump heights were obtained using a Vertec jump device (Swift Performance Equipment, Lismore, Australia). Stationary vertical jump height was recorded using a stationary bilateral countermovement jump, whereas the DVJ was performed off the players outside foot after a 5-m straight line run-up. This was completed for both D and ND foot takeoff, with foot dominance being defined as the players preferred kicking foot. At the highest point of each jump, players were instructed to displace the vanes of the Vertec, with the highest vane displaced by the inside hand being recorded. Final jump height was recorded (stationary and dynamic) as the difference between standing height (obtained before completing both jumps) and the highest vane displaced whilst jumping. Three trials were completed for each jump (stationary/dynamic) with the maximum jump height obtained being used as the criterion value for analysis.
Timing lights (Swift Performance Equipment) were used to measure each player's 20-m sprint time, with gates being placed at the start line, and 20 m distance, 1.5 m wide. Players commenced the sprint when they were ready in a stationary upright position, with their lead foot on the start line, eliminating reaction time. Times were recorded to the nearest 0.01 seconds, the fastest 20 m time of 3 trials was used as the criterion values for analysis.
Australian Football League Agility Test
The same agility test as described by Young and Pryor (35) was used. As shown in Figure 1, this test required the players to maneuver as fast as possible around five 1.1-m high poles, each with a circumference of 12 cm. If a pole was displaced during the test, the trial was abandoned and restarted after 1 minute. Players were not allowed to touch the ground with their hand when changing direction, with the trial being abandoned if this occurred. Timing lights were placed 1.5 m wide and were positioned at the start and finish of the test. The fastest of the 3 trials was used as the criterion value for analysis, with times being recorded to the nearest 0.01 seconds.
Repeat Sprint Test
Repeated sprint ability was assessed using six, 30-m repeated sprints at maximal effort, leaving on a 20 seconds cycle (20). Timing lights were placed at the start and finish lines, 1.5 m wide with players sprinting in both directions (3 sprints in each direction). A stationary start line was positioned 0.5 m behind each set of timing lights and players were given 10 m (marked with a cone) to decelerate after each sprint. Players then walked back to the start position to prepare for the next sprint. Timing signals, including a 10 seconds and 5 seconds warning and the starting beep, were emitted from a prerecorded MP3 audio broadcast. Times were recorded to the nearest 0.01 seconds, and the cumulative time of all 6 repeated sprints was used as the criterion variable for analysis.
Maximal Aerobic Capacity
The 20-m multistage fitness test was used to estimate player's maximal aerobic capacity (10). Players were required to continually run back and forth along a 20 m distance, keeping in time with a monotonic “beep” emitted by a compact disk. The time between each beep (shuttle) gradually decreased as the test (or levels) progressed; requiring players to incrementally increase their running speed. The test was concluded when the player either reached volitional exhaustion or was unable to keep time with the beeps on 2 consecutive occasions. The highest level and shuttle successfully obtained by each player was used as the criterion value for analysis.
Athletic Movement Skill Assessment
The players performed the same athletic movement protocol as described by Woods et al. (30). This assessment included an overhead squat, double lunge and single-leg Romanian deadlift (both movements performed on both left and right legs), and a push-up. This athletic movement assessment has been discriminately validated for use in the comparison of athletic movement competence between elite junior and senior AF players (30). The overhead squat, double lunge and single-leg Romanian deadlifts were all performed with a wooden dowel to assist with the anatomical positioning of the player's limbs when performing these movements. No feedback or verbal encouragement was provided to the players during the movement production in an attempt to limit a potential scoring bias effect (11). Each movement was scored across 3 assessment points using a 3-point scale (maximum of 9 points); with each point being anchored to a description of the movement characteristics (16,30). Each movement was performed for a total of 5 repetitions, except for the push-up, which had specific repetition targets embedded within the scoring criteria. The scoring criteria are presented in Table 1. Each movement was scored retrospectively by 1 researcher using video-recorded footage acquired using a standard 2-dimensional camera (HDR-XR260VE; Sony, Kōnan, Minato, Tokyo) placed in optimal positions for assessment (sagittal and frontal). The total scores for each movement (maximum of 9) were used as the criterion variables for analysis.
To establish the measurement properties of the athletic movement skill scoring procedure, the intrarater reliability was assessed. The entire U18 sample was scored on 2 separate occasions by the same researcher, separated by 7 days. Given the categorical nature of the scoring process, the level of agreement between the 2 scoring occasions was assessed using the weighted kappa statistic (κ), with the level of agreement defined as follows: <0 less than chance agreement, 0.01–0.20 slight agreement, 0.21–0.40 fair agreement, 0.41–0.60 moderate agreement, 0.61–0.80 substantial agreement, and 0.81–0.99 almost perfect agreement (15).
Descriptive statistics (mean ± SD) were calculated for all physical fitness, anthropometric and athletic movement skill criterions. The effect size of developmental level (2 levels: U16 and U18) on each test criterion was calculated using Cohen's d statistic; where an effect size of d = 0. 10–0.20 was considered small, d = 0.21–0.50 moderate, d = 0.51–0.80 large, and d > 0.80 very large (8). After this, a multivariate analysis of variance (MANOVA) was used to test the main effect of developmental level on the test criterions with the type-I error rate set at α < 0.05. All between-group comparisons were conducted using SPSS (version 21; SPSS, Inc., USA).
After this, binary logistic regression models were built to identify the test criterions yielding the greatest association with the main effect of developmental level. Each test criterion that significantly differed according to the MANOVA was coded as an explanatory variable, and the developmental level was coded as the binary response variable (1 = U18, 0 = U16). All modeling was performed using the computing environment R (Version 3.1.3 R Core Team, 2015). Model parsimony was found by reducing the full model using the “dredge” function in the MuMIn package (6). This function returns the best model using Akaike's information criterion. Further, to ensure the strength of the model fit, a null model was built and used as a comparator.
Finally, to assess the discriminative ability of the most parsimonious model and its single term predictors, the pROC package (22) was used to conduct a sensitivity vs. specificity analysis. Receiver operating characteristic (ROC) curves were built, with the area under the curve (AUC) being calculated. An AUC of 1 (100%) represents perfect discriminative power for a binary response variable. For each model, the point on the curve that generated the highest AUC was considered the value at which a “cut off” might be acceptable for discriminating the 2 developmental levels and, thus, the “benchmark” value for which coaches could base their training interventions designed to reduce potential developmental gaps.
The level of agreement between the 2 scoring sessions for the athletic movement skill assessment ranged from “substantial” to “almost perfect” for each movement. There was a significant effect of developmental level on the physical fitness, anthropometric and athletic movement skill criterions (V = 0.498, F = 4.031, p ≤ 0.05). Specifically, there was a significant effect of developmental level on body mass, SVJ, DVJD, DVJND, 20-m sprint, agility, repeated sprints, 20-m multistage fitness test, and the push-up movement (Table 2). On average, the U18 group performed each of these assessments with a higher level of proficiency than the U16 group (Table 2). Further, body mass, SVJ, DVJD, DVJND, agility, and the push-up movement demonstrated large to very large effect sizes (Table 2).
These 9 test criterions were then included as explanatory variables within the full binary logistic regression model. However, of these, body mass, DVJND, repeated sprints, and the 20-m multistage fitness test were retained in the best reduced model (Table 3).
The ROC curve for this full model was maximized when the combined score of these 4 explanatory variables equaled 180.7 (AUC = 79.3%; Figure 2). Of the U18 players, 33 (89%) recorded a combined score of ≥180.7, whereas of the U16 players, 16 (40%) were deemed misclassified because of recording a combined score of ≥180.7. Thus, the full model detected 89% of the true positives (U18 players) and 60% of the true negatives (U16 players).
From the explanatory variables included in the full model, DVJND height provided the greatest between-group discrimination (AUC = 74.6%; Figure 3B). Of the 37 U18 players, 21 (57%) recorded a DVJND height of 71.5 cm or greater, whereas only 8 (20%) of the U16 players recorded a DVJND height of 71.5 cm or greater. Accordingly, a DVJND height of 71.5 cm detected 57% and 80% of the U18 and U16 players, respectively. The next best single term variable to provide developmental discrimination was repeat sprint time (AUC = 73.1%; Figure 3C), followed by body mass (AUC = 67.2%; Figure 3A), and finally the score obtained on the 20-m multistage fitness test (AUC = 65%; Figure 3D).
This study aimed to identify the physical fitness, anthropometric and athletic movement qualities discriminant of developmental level in junior AF. Given their longitudinal exposure to an opportunistic learning environment, it was hypothesized that the U18 players would possess superior performance qualities relative to their U16 counterparts. The results partially supported this hypothesis, with them being the combination of body mass, DVJND height, repeat sprint time, and score on the 20-m multistage fitness test that provided the greatest association with developmental level. Specifically, a combined score of 180.7 successfully discriminated 89% and 60% of the U18 and U16 players, respectively. This finding is in general agreement with work that has shown the utility of these measures for identifying talent at the U18 level (13,18,31). Thus, AF coaches at the U16 level may consider implementing training interventions oriented around increasing their players' lower body power, repeated sprint ability, and maximal aerobic capacity. This may not only improve the developmental transition to the U18 level, but may also increase a player's prospective likelihood of being talent identified at the U18 level. However, the development of players' movement competency may be a more immediate concern for coaches given the low scores observed at both developmental levels. It is imperative that coaches aim to increase athletic movement skill in junior AF players, as poorly developed movement competency may inhibit a player's ability to tolerate advanced training demands, limiting their potential to progress through the talent pathway (17).
Of note was that the most parsimonious full model did not retain measures of athletic movement skill. Consequently, despite the reported relationship between athletic movement competency and physical performance outcome assessments (17,33), it seems that the U18 player's superior physical fitness qualities were not operationalized by a superior athletic movement competency in this study. Rather, both the U16 and U18 players in this study demonstrated a relatively low movement competency when compared with values obtained by senior AFL players (30). It could be concluded that the U18 player's superior physical fitness reflects a potentially prolonged exposure to targeted training interventions oriented around physical performance outcomes. Specifically, work has demonstrated a gap in the physical requirements of game-play between the U18 and AFL levels (5). Presumably, coaches at the U18 level are designing physical training interventions based on these differences, with the aim of improving the U18 to AFL transition.
It is important to acknowledge the potential maturation differences between developmental levels, which could account for the observed differences. Gastin et al. (12) demonstrated that biological maturation correlated with score on the 20-m multistage fitness test and 20-m sprint time in junior AF players aged U11 to U19. However, the within-age grouping influences of biological maturation on running performance were more pronounced in players chronologically aged below 15 years (12). Consequently, given that the age of our players ranged from 15.6 to 17.9 years, it is unlikely that biological maturation was the primary mechanism underpinning the differences between the 2 developmental levels in this instance. Nonetheless, biologically mature juniors within the same age category may be at a performance advantage relative to those who have not yet reached biological maturity (12). Thus, future work should investigate the influence of biological maturation on the production of physical fitness and athletic movement qualities in the elite junior AF talent pathway.
Given the multidimensionality of AF game-play, temporal constraints often limit the amount of time allocated to the development of physical performance qualities. These results suggest that if coaches at the U16 level cannot explicitly allocate training time to the 3 physical fitness qualities included in the full regression model, they may wish to prioritize the development of a player's DVJ, given this criterion demonstrated the greatest single term developmental discrimination (Figure 3B). Interestingly, a secondary finding of this study was the discrepancy between the D and ND foot jump heights; with it appearing less pronounced at the U18 level compared with the U16 level (Table 2). As the dynamic jumping action is multifactorial in nature, the asymmetry of the U16 players may indicate that the U18 players possess greater strength, power, and coordination in their lower body (14,23,25). Importantly, increases in maximal strength and power of the lower body are known to enhance performance tasks such as running and jumping (3,9). Therefore, it may be beneficial for U16 players to engage in strength training interventions that incorporate bilateral and unilateral lower-body exercises with the aims of enhancing performance and reducing any observed bilateral deficit between D and ND limbs when progressing toward the U18 level. When translated to an on-field performance, a greater DVJ may contribute to success in aerial ball contests, with these players potentially being viewed advantageously by talent recruiters (31). However, the considerably poor performances noted at both developmental levels for the athletic movement assessment suggest that coaches in the AF talent pathway should look toward improving the athletic movement qualities of players before integrating targeted strength training interventions. Further, improving the efficiency of production with regards to athletic movement qualities, such as the overhead head, may innately augment the development of physical fitness qualities, such as jump height, and/or sprint time (17).
Repeat sprint ability is a critical physical quality for players to possess in junior and senior AF competitions given the intermittent nature of game-play (5). However, the gap between elite junior and senior AF competitions with regards to the number of repeated efforts performed during game-play seems to be increasing (5). Notably, Burgess et al. (5) reported that AFL players generated greater maximum running velocities and performed more repeated sprint efforts per minute of game-play relative to players in an elite U18 competition. The results of this study emphasize the need for training interventions that develop high anaerobic capacities and repeated sprint efforts at the U16 level. Further, because the aim of a State Academy is to minimize performance discrepancies between elite junior and senior levels, there is a need to enhance the physical capacities of U18 and U16 players to allow them to compete at the elite senior level (4). It is speculated that the difference noted here with regards to repeat sprint ability is a product of training environment. For example, it is hypothesized that players at the U18 level partook in more physically focused anaerobic training drills relative to their U16 counterparts, as coaches at the U18 level aimed to prepare players for the rigors of elite senior competitions (i.e., the AFL). Given this speculation, it would be of interest for future work to investigate the developmental histories of players in the AF talent pathway. By doing so, it may assist with the explanation of the superior performance qualities observed by the U18 players in this study.
The results from this study show that estimated maximal aerobic capacity varies significantly between developmental levels within the junior talent pathway. Despite not yet being elucidated within the literature, it could be suggested that the difference noted in scores obtained for the 20-m multistage fitness test is reflective of the physical requirements of game-play at the U16 and U18 levels. Specifically, players at the U18 level may be more equipped to cover greater running distances during game-play given their superior maximal aerobic capacity (as estimated with the 20-m multistage fitness test). Developing this physical quality at the U16 level may afford players at this initial stage of development with an advantageous base for which to manage the potential increased aerobic requirements of U18 game-play.
Despite the high level of between-group discrimination noted for the full regression model, of particular interest were the 16 misclassified U16 players. This indicates that these misclassified U16 players were potentially already physically equipped to participate in the U18 competition given their combination of physical performance qualities. Consequently, in addition to being used to drive developmental practices at the U16 level, the combination of these 4 assessments could be used as an initial identification of “readiness” for U16 players to enter the U18 level. With that said, it is important to note that this study only investigated 1 element of effective game-play in AF (physicality). Whilst an advantageous level of physicality is required in AF, overall success in the game is underpinned by multidimensional performance qualities (physical, technical, and perceptual) (32). Future work should progress the results presented here and work toward the identification of the multidimensional performance qualities discriminant of developmental level in the junior AF talent pathway.
In conclusion, this work demonstrates that talent-identified U18 AF players possess superiorities in certain physical fitness and anthropometric qualities relative to their U16 counterparts. It was the combination of body mass, DVJND height, repeat sprint time, and score on the 20-m multistage fitness test that provided the greatest developmental discrimination. Given these findings, future work should look to identify the technical and/or perceptual qualities discriminant of developmental level in junior AF. This may operationalize the establishment of a multidiemsnional approach to TDE in elite junior AF.
There are 3 main practical applications to stem from this work. First, coaches at the U16 level should look to design training interventions explicitly focused on the development of DVJ height, repeated sprint ability, and maximal aerobic capacity. This may assist with the U16 to U18 developmental transition, which may ultimately lead to a smoother U18 to AFL transition. To further this, coaches may wish to consider improving the athletic movement competency of their players, which may ultimately augment the development of desired physical fitness qualities. Second, given foreseeable temporal constraints limiting the focus on all 3 physical fitness qualities, coaches at the U16 level may wish to target the development of lower-body power and coordination to assist with the development of dynamic jumping capability. Given the ROC curve analysis performed in this study, coaches could use a DVJND height of 71.5 cm as an appropriate developmental benchmark for U16 players looking to progress into the U18 level. Last, the binary logistic regression model built in this study may provide coaches with an initial means of which to identify U16 players capable of managing the physical requirements of game-play at the U18 level.
1. Abernethy B. Introduction: Developing expertise in sport—How can research inform practice. In: Developing Sport Expertise: Researchers and Coaches Put Theory into Practice. Farrow D, Baker J, MacMahon C, eds. Abingdon, United Kingdom: Routledge, 2008. pp. 1–14.
2. Bilsborough JC, Greenway KG, Opar DA, Livingstone SG, Cordy JT, Bird SR, Coutts AJ. Comparison of anthropometry, upper-body strength, and lower-body power characteristics in different levels of Australian football players. J Strength Cond Res 29: 826–834, 2015.
3. Bloomquist K, Langberg H, Karlsen S, Madsgaard S, Boesen M, Raastad T. Effect of range of motion in heavy load squatting on muscle and tendon adaptations. Eur J Appl Physiol 113: 2133–2142, 2013.
4. Burgess D, Naughton G, Hopkins W. Draft-camp predictors of subsequent career success in the Australian football league. J Sci Med Sport 15: 561–567, 2012.
5. Burgess D, Naughton G, Norton K. Quantifying the gap between under 18 and senior AFL football: 2003-2009. Int J Sports Physiol Perform 7: 53–58, 2012.
6. Burnham KP, Anderson DR. Model Selection and Multimodel Interface: A Practical Information-theoretic Approach (2nd ed.). New York, NY: Springer-Verlag, 2002.
7. Buttifant D. Physiological and performance characteristics of Australian football league players. J Sport Sci 17: 808–809, 1999.
8. Cohen J. Behavioural Sciences. New Jersey: Lawrence Erlbaum Associates Inc., 1988.
9. Comfort P, Haigh A, Matthews MJ. Are changes in maximal squat strength during preseason training reflected in changes in sprint performance in rugby league players? J Strength Cond Res 26: 772–776, 2012.
10. Ellis L, Gastin PB, Lawrence S, Savage B, Buckeridge A, Stapff A. Protocols for the physiological assessment of team sport players. In: Physiological Tests for Elite Athletes. Gore CG, ed. Champaign, IL: Human Kinetics, 2000.
11. Frost DM, Beach TA, Callaghan JP, McGill SM. FMS scores change with performers' knowledge of the grading criteria-are general whole-body movement screens capturing “dysfunction”? J Strength Cond Res 29: 3037–3044, 2015.
12. Gastin PB, Bennett G, Cook J. Biological maturity influences running performance in junior Australian football. J Sci Med Sport 16: 140–145, 2013.
13. Keogh J. The use of physical fitness scores and anthropometric data to predict selection in an elite under 18 Australian rules football team. J Sci Med Sport 2: 125–133, 1999.
14. Kollias I, Hatzitaki V, Papaiakovou G, Giatsis G. Using principal components analysis to identify individual differences in vertical jump performance. Res Q Exerc Sport 72: 63–67, 2001.
15. Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics 33: 159–174, 1977.
16. McKeown I, Taylor-McKeown K, Woods C, Ball N. Athletic ability assessment: A movement assessment protocol for athletes. Int J Sports Phys Ther 9: 862–873, 2014.
17. Parsonage JR, Williams RS, Rainer P, McKeown I, Williams MD. Assessment of conditioning-specific movement tasks and physical fitness measures in talent identified under 16-year-old rugby union players. J Strength Cond Res 28: 1497–1506, 2014.
18. Pyne DB, Gardner AS, Sheehan K, Hopkins WG. Fitness testing and career progression in AFL football. J Sci Med Sport 8: 321–332, 2005.
19. Pyne DB, Gardner AS, Sheehan K, Hopkins WG. Positional differences in fitness and anthropometric characteristics in Australian football. J Sci Med Sport 9: 143–150, 2006.
20. Pyne DB, Saunders PU, Montgomery PG, Hewitt AJ, Sheehan K. Relationships between repeated sprint testing, speed, and endurance. J Strength Cond Res 22: 1633–1637, 2008.
21. Regnier G, Salmela J, Russell SJ. Talent Detection and Development in Sport. New York, NY: Macmillan, 1993.
22. Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez JC, Muller M. pROC: An open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics 12: 77, 2011.
23. Stephens TM II, Lawson BR, DeVoe DE, Reiser RF II. Gender and bilateral differences in single-leg countermovement jump performance with comparison to a double-leg jump. J Appl Biomech 23: 190–202, 2007.
24. Till K, Jones B, Darrall-Jones J, Emmonds S, Cooke C. Longitudinal development of anthropometric and physical characteristics within academy rugby league players. J Strength Cond Res 29: 1713–1722, 2015.
25. Tomoika M, Owings TM, Grabiner MD. Lower extremity strength and coordination are independent contributors to maximum vertical jump height. J Appl Biomech 17: 181–187, 2001.
26. Vaeyens R, Gullich A, Warr CR, Philippaerts R. Talent identification
and promotion programs of Olympic athletes. J Sport Sci 27: 1367–1380, 2009.
27. Vaeyens R, Malina RM, Janssens M, Van Renterghem B, Bourgois J, Vrijens J, Philippaerts RM. A multidisciplinary selection model for youth soccer: The ghent youth soccer project. Br J Sports Med 40: 928–934, 2006.
28. Veale JP, Pearce AJ, Buttifant D, Carlson JS. Anthropometric profiling of elite junior and senior Australian football players. Int J Sports Physiol Perform 5: 509–520, 2010.
29. Williams AM, Reilly T. Talent identification
and development in soccer. J Sport Sci 18: 657–667, 2000.
30. Woods CT, McKeown I, Haff GG, Robertson S. Comparison of athletic movement between elite junior and senior Australian football players. J Sport Sci 34: 1260–1265, 2016.
31. Woods CT, Raynor AJ, Bruce L, McDonald Z, Collier N. Predicting playing status in junior Australian football using physical and anthropometric parameters. J Sci Med Sport 18: 225–229, 2015.
32. Woods CT, Raynor AJ, Bruce L, McDonald Z, Robertson S. The application of a multi-dimensional assessment approach to talent identification
in Australian football. J Sport Sci 34: 1340–1345, 2016.
33. Young WB, Grace S, Talpey S. Association between leg power and sprinting technique with 20-m sprint performance in elite junior Australian football players. Int J Sports Sci Coach 9: 1153–1160, 2014.
34. Young WB, Pryor L. Relationship between pre-season anthropometric and fitness measures and indicators of playing performance in elite junior Australian Rules football. J Sci Med Sport 10: 110–118, 2007.