Carl T. Woods

Predicting higher selection in elite junior Australian Rules football: the influence of physical performance and anthropometric attributes

OBJECTIVES To develop a physiological performance and anthropometric attribute model to predict Australian Football League draft selection. DESIGN Cross-sectional observational. METHODS Data was obtained (n=4902) from three Under-18 Australian football competitions between 2010 and 2013. Players were allocated into one of the three groups, based on their highest level of selection in their final year of junior football (Australian Football League Drafted, n=292; National Championship, n=293; State-level club, n=4317). Physiological performance (vertical jumps, agility, speed and running endurance) and anthropometric (body mass and height) data were obtained. Hedges effect sizes were calculated to assess the influence of selection-level and competition on these physical attributes, with logistic regression models constructed to discriminate Australian Football League Drafted and National Championship players. Rule induction analysis was undertaken to determine a set of rules for discriminating selection-level. RESULTS Effect size comparisons revealed a range of small to moderate differences between State-level club players and both other groups for all attributes, with trivial to small differences between Australian Football League Drafted and National Championship players noted. Logistic regression models showed multistage fitness test, height and 20 m sprint time as the most important attributes in predicting Draft success. Rule induction analysis showed that players displaying multistage fitness test scores of >14.01 and/or 20 m sprint times of <2.99 s were most likely to be recruited. CONCLUSIONS High levels of performance in aerobic and/or speed tests increase the likelihood of elite junior Australian football players being recruited to the highest level of the sport.

The application of a multi-dimensional assessment approach to talent identification in Australian football

ABSTRACT This study investigated whether a multi-dimensional assessment could assist with talent identification in junior Australian football (AF). Participants were recruited from an elite under 18 (U18) AF competition and classified into two groups; talent identified (State U18 Academy representatives; n = 42; 17.6 ± 0.4 y) and non-talent identified (non-State U18 Academy representatives; n = 42; 17.4 ± 0.5 y). Both groups completed a multi-dimensional assessment, which consisted of physical (standing height, dynamic vertical jump height and 20 m multistage fitness test), technical (kicking and handballing tests) and perceptual-cognitive (video decision-making task) performance outcome tests. A multivariate analysis of variance tested the main effect of status on the test criterions, whilst a receiver operating characteristic curve assessed the discrimination provided from the full assessment. The talent identified players outperformed their non-talent identified peers in each test (P < 0.05). The receiver operating characteristic curve reflected near perfect discrimination (AUC = 95.4%), correctly classifying 95% and 86% of the talent identified and non-talent identified participants, respectively. When compared to single assessment approaches, this multi-dimensional assessment reflects a more comprehensive means of talent identification in AF. This study further highlights the importance of assessing multi-dimensional performance qualities when identifying talented team sports.

What are talent scouts actually identifying? Investigating the physical and technical skill match activity profiles of drafted and non-drafted U18 Australian footballers

OBJECTIVES To compare the physical and technical skill match activity profiles of drafted and non-drafted under 18 Australian football players. DESIGN Cross-sectional observational. METHODS In-game physical and skill variables were assessed for under 18 Australian football players participating within the 2013 and 2014 National under 18 Australian Football League Championships. Players originated from one State Academy (n=55). Ten games were analysed; resulting in 183 observations. Players were sub-divided into two groups; drafted/non-drafted. Microtechnology and a commercial statistical provider allowed the quantification of total distance (m), relative distance (mmin(-1)), high speed running distance (>15kmh(-1)), high speed running expressed as a percentage of total distance (% total), total disposals, marks, contested possessions, uncontested possessions, inside 50s and rebound 50s (n=10). The effect size (d) of draft outcome on these criterion variables was calculated, with generalised estimating equations (GEEs) used to model which of these criterion variables was associated with draft outcome. RESULTS Contested possessions and inside 50s reflected large effect size differences between groups (d=1.01, d=0.92, respectively). The GEE models revealed contested possessions as the strongest predictor of draft outcome, with inside 50s being the second. Comparatively, the remaining criterion variables were not predictive of draft outcome. CONCLUSIONS Contested possessions and inside 50s are the most influential in-game variables associated with draft outcome for West Australian players competing within the National under 18 Australian Football League Championships. Technically skilled players who win contested possessions and deliver the ball inside 50 may be advantageously positioned for draft success.

Discriminating talent-identified junior Australian football players using a video decision-making task

Abstract This study examined if a video decision-making task could discriminate talent-identified junior Australian football players from their non-talent-identified counterparts. Participants were recruited from the 2013 under 18 (U18) West Australian Football League competition and classified into two groups: talent-identified (State U18 Academy representatives; n = 25; 17.8 ± 0.5 years) and non-talent-identified (non-State U18 Academy selection; n = 25; 17.3 ± 0.6 years). Participants completed a video decision-making task consisting of 26 clips sourced from the Australian Football League game-day footage, recording responses on a sheet provided. A score of “1” was given for correct and “0” for incorrect responses, with the participants total score used as the criterion value. One-way analysis of variance tested the main effect of “status” on the task criterion, whilst a bootstrapped receiver operating characteristic (ROC) curve assessed the discriminant ability of the task. An area under the curve (AUC) of 1 (100%) represented perfect discrimination. Between-group differences were evident (P < 0.05) and the ROC curve was maximised with a score of 15.5/26 (60%) (AUC = 89.0%), correctly classifying 92% and 76% of the talent-identified and non-talent-identified participants, respectively. Future research should investigate the mechanisms leading to the superior decision-making observed in the talent-identified group.

Comparison of athletic movement between elite junior and senior Australian football players

ABSTRACT This study compared the athletic movement skill between elite Under-18 (U18) Australian football (AF) and senior Australian Football League (AFL) players. The U18 sample (n = 13; 17.7 ± 0.6 years) were representatives of an elite talent development programme. The AFL players were classified accordingly; Group 1 (1–4 AFL seasons; n = 20; 21.2 ± 1.9 years) and Group 2 (>5 AFL seasons; n = 14; 26.3 ± 2.6 years). Participants performed an athletic movement skill assessment, inclusive of five foundational movements. Each movement was scored across three assessment points using a three-point scale. Total score for each movement (maximum of nine) and overall score (maximum of 63) were used as criteria. Multivariate analysis of variance (MANOVA) was used to test the effect of developmental group (three levels) on the criteria. Receiver operating curves were built to examine the discriminant capability of the overall score. A significant effect of developmental group was noted, with the U18 sample having a lower mean total score for four of the five movements. Overall scores of 49/63 and 50/63 discriminated the elite U18 sample from Group 1 and Group 2, respectively. U18 players may have less developed athletic movement skills when compared to their senior AFL counterparts.

Modelling age-related changes in motor competence and physical fitness in high-level youth soccer players: implications for talent identification and development

ABSTRACT Purpose: The effectiveness of early talent identification and development programs in soccer is questionable due to the dynamic nature of these processes in young and adolescent players. To date, only a few studies have longitudinally modelled developmental trajectories of functional characteristics in youth soccer players, yet none have captured the entire typical age range of soccer development programs (5–20 years). Furthermore, these studies have often failed to take into account the multidimensional nature of talent identification and development processes. Methods: This study used segmented linear models to map the periods of accelerated and decelerated development of motor competence and physical fitness in a large sample (2228 players with 6120 observations) of high level Belgian youth soccer players between 5–20 years. Results: The segmented models revealed that motor competence showed faster development well before the average estimated Age at Peak Height Velocity. Agility, lower body explosive power, intermittent endurance, and straight line running speed showed continuous development that does not slow down until players are between 15–17 years old. Conclusion: This study highlights the dynamic nature of talent development and provides practical considerations for those involved in talent identification and development programs in youth soccer.

Team Performance Indicators Explain Outcome during Women’s Basketball Matches at the Olympic Games

The Olympic Games is the pinnacle international sporting competition with team sport coaches interested in key performance indicators to assist the development of match strategies for success. This study examined the relationship between team performance indicators and match outcome during the women’s basketball tournament at the Olympic Games. Team performance indicators were collated from all women’s basketball matches during the 2004–2016 Olympic Games (n = 156) and analyzed via linear (binary logistic regression) and non-linear (conditional interference (CI) classification tree) statistical techniques. The most parsimonious linear model retained “defensive rebounds”, “field-goal percentage”, “offensive rebounds”, “fouls”, “steals”, and “turnovers” with a classification accuracy of 85.6%. The CI classification tree retained four performance indicators with a classification accuracy of 86.2%. The combination of “field-goal percentage”, “defensive rebounds”, “steals”, and “turnovers” provided the greatest probability of winning (91.1%), while a combination of “field-goal percentage”, “steals”, and “turnovers” provided the greatest probability of losing (96.7%). Shooting proficiency and defensive actions were identified as key team performance indicators for Olympic female basketball success. The development of key defensive strategies and/or the selection of athletes highly proficient in defensive actions may strengthen Olympic match success. Incorporation of non-linear analyses may provide teams with superior/practical approaches for elite sporting success.

Identifying the Physical Fitness, Anthropometric and Athletic Movement Qualities Discriminant of Developmental Level in Elite Junior Australian Football: Implications for the Development of Talent

Abstract Gaudion, SL, Doma, K, Sinclair, W, Banyard, HG, and Woods, CT. Identifying the physical fitness, anthropometric and athletic movement qualities discriminant of developmental level in elite junior Australian football: implications for the development of talent. J Strength Cond Res 31(7): 1830–1839, 2017—This study aimed to identify the physical fitness, anthropometric and athletic movement qualities discriminant of developmental level in elite junior Australian football (AF). From a total of 77 players, 2 groups were defined according to their developmental level; under 16 (U16) (n = 40, 15.6 to 15.9 years), and U18 (n = 37, 17.1 to 17.9 years). Players performed a test battery consisting of 7 physical fitness assessments, 2 anthropometric measurements, and a fundamental athletic movement assessment. A multivariate analysis of variance tested the main effect of developmental level (2 levels: U16 and U18) on the assessment criterions, whilst binary logistic regression models and receiver operating characteristic (ROC) curves were built to identify the qualities most discriminant of developmental level. A significant effect of developmental level was evident on 9 of the assessments (d = 0.27–0.88; p ⩽ 0.05). However, it was a combination of body mass, dynamic vertical jump height (nondominant leg), repeat sprint time, and the score on the 20-m multistage fitness test that provided the greatest association with developmental level (Akaikes information criterion = 80.84). The ROC curve was maximized with a combined score of 180.7, successfully discriminating 89 and 60% of the U18 and U16 players, respectively (area under the curve = 79.3%). These results indicate that there are distinctive physical fitness and anthropometric qualities discriminant of developmental level within the junior AF talent pathway. Coaches should consider these differences when designing training interventions at the U16 level to assist with the development of prospective U18 AF players.

The use of player physical and technical skill match activity profiles to predict position in the Australian Football League draft

ABSTRACT This study investigated the extent to which position in the Australian Football League (AFL) national draft is associated with individual game performance metrics. Physical/technical skill performance metrics were collated from all participants in the 2014 national under 18 (U18) championships (18 games) drafted into the AFL (n = 65; 17.8 ± 0.5 y); 232 observations. Players were subdivided into draft position (ranked 1–65) and then draft round (1–4). Here, earlier draft selection (i.e., closer to 1) reflects a more desirable player. Microtechnology and a commercial provider facilitated the quantification of individual game performance metrics (n = 16). Linear mixed models were fitted to data, modelling the extent to which draft position was associated with these metrics. Draft position in the first/second round was negatively associated with “contested possessions” and “contested marks”, respectively. Physical performance metrics were positively associated with draft position in these rounds. Correlations weakened for the third/fourth rounds. Contested possessions/marks were associated with an earlier draft selection. Physical performance metrics were associated with a later draft selection. Recruiters change the type of U18 player they draft as the selection pool reduces. juniors with contested skill appear prioritised.

A comparison of the physical and anthropometric qualities explanatory of talent in the elite junior Australian football development pathway

OBJECTIVES To compare the physical and anthropometric qualities explanatory of talent at two developmental levels in junior Australian football (AF). DESIGN Cross-sectional observational. METHODS From a total of 134 juniors, two developmental levels were categorised; U16 (n=50; 15.6±0.3 y), U18 (n=84; 17.4±0.5 y). Within these levels, two groups were a priori defined; talent identified (U16; n=25; 15.7±0.2 y; U18 n=42; 17.5±0.4 y), non-talent identified (U16; n=25; 15.6±0.4 y; U18; n=42; 17.3±0.6 y). Players completed seven physical and anthropometric assessments commonly utilised for talent identification in AF. Binary logistic regression models were built to identify the qualities most explanatory of talent at each level. RESULTS A combination of standing height, dominant leg dynamic vertical jump height and 20m sprint time provided the most parsimonious explanation of talent at the U16 level (AICc=60.05). At the U18 level, it was a combination of body mass and 20m sprint time that provided the most parsimonious explanation of talent (AICc=111.27). CONCLUSIONS Despite similarities, there appears to be distinctive differences in physical and anthropometric qualities explanatory of talent at the U16 and U18 level. Coaches may view physical and anthropometric qualities more (or less) favourably at different levels of the AF developmental pathway. Given these results, future work should implement a longitudinal design, as physical and/or anthropometric qualities may deteriorate (or emerge) as junior AF players develop.