Objectives Football Federation Australia (FFA) has identified that Australian athletes are proficient physically, however often lack the technical and tactical skills to excel internationally. The aim of the current study was to assess if a video-based decision-making test could discriminate different age-matched skill levels of talent in Australian soccer. Design Cross-sectional observational. Methods Sixty-two youth male soccer players completed a video-based decision-making test. Results An ANOVA test showed that the video-based test significantly discriminated between all three groups, with the national elite athletes selecting more correct responses than the state elite (65.3 ± 8.1%; 56.0 ± 9.1%, respectively). The state elite were more accurate than the sub-elite (45.9 ± 8.8%). Conclusions Results suggest that a video-based test may be a suitable tool to use in the selection of athletes as a measure of decision-making skill. The low accuracy scores, even for the national elite cohort, suggest that decision-making skill at the youth level has room for improvement and should be prioritised as an area for development.

Objectives: To determine whether the technical ability of Australian youth soccer players could distinguish between various playing levels. Design: A cross-sectional observational design was used with each player required to complete four technical tests. Methods: Sixty-two participants were representatives of three cohorts of youth soccer in Australia: national elite (n¼18), state elite (n¼22) and sub-elite (n¼22). The technical tests used were Loughborough Short Passing Test (LSPT), long passing test (LPT), shooting test and speed dribbling, with all players familiarised with the tests prior to data collection. Differences between cohorts were analysed using a multiple analysis of variance test with follow-up analyses of variance and Tukey Honest Significant Difference post-hoc test, which were subsequently used to inform a sensitivity analysis, more specifically a bootstrapped receiver operating curve to determine cut-off scores for each variable. Results: The national elite cohort scored better than state- and sub-elite cohorts on the LSPT, however, the state elite produced the fastest time before penalties. The sub-elite cohort scored less points on the LPT compared to both national- and state elite cohorts, on both feet. In regards to speed dribbling, national elite players were faster than both the state- and sub-elite cohorts. Shooting accuracy and velocity were able to discriminate the national- and sub-elite cohorts on the dominant foot, with shooting velocity on the nondominant foot being faster for the national elite compared to both the state- and sub-elite cohorts. Conclusions: A number of differences in technical ability were identified between varying levels of Australian youth soccer players. Youth soccer coaches and sports scientists should use the cut-off scores for the technical tests in the talent identification and development process, with aspiring players aiming to reach these levels

Woods, CT, Keller, BS, McKeown, I, and Robertson, S. A comparison of athletic movement among talent-identified juniors from different football codes in Australia: implications for talent development. J Strength Cond Res 30(9): 2440-2445, 2016-This study aimed to compare the athletic movement skill of talent-identified (TID) junior Australian Rules football (ARF) and soccer players. The athletic movement skill of 17 TID junior ARF players (17.5-18.3 years) was compared against 17 TID junior soccer players (17.9-18.7 years). Players in both groups were members of an elite junior talent development program within their respective football codes. All players performed an athletic movement assessment that included an overhead squat, double lunge, single-leg Romanian deadlift (both movements performed on right and left legs), a push-up, and a chin-up. Each movement was scored across 3 essential assessment criteria using a 3-point scale. The total score for each movement (maximum of 9) and the overall total score (maximum of 63) were used as the criterion variables for analysis. A multivariate analysis of variance tested the main effect of football code (2 levels) on the criterion variables, whereas a 1-way analysis of variance identified where differences occurred. A significant effect was noted, with the TID junior ARF players outscoring their soccer counterparts when performing the overhead squat and push-up. No other criterions significantly differed according to the main effect. Practitioners should be aware that specific sporting requirements may incur slight differences in athletic movement skill among TID juniors from different football codes. However, given the low athletic movement skill noted in both football codes, developmental coaches should address the underlying movement skill capabilities of juniors when prescribing physical training in both codes.

Objectives: To determine whether Australian youth soccer players of varying levels could be distinguished based on their anthropometrical and physical attributes. Design: A cross-sectional observational design was used, involving six anthropometrical and physical tests for each player. Methods: Participants represented three youth levels of competition, namely national elite (n ¼ 18), state elite (n ¼ 22) and sub-elite (n ¼ 22). Anthropometrical and physical tests included standing height; body mass; 5, 10, 30 m sprint and 20 m 'flying start' sprint; zigzag agility test; vertical jump and Yo-Yo Intermittent Recovery test level 1. A multiple analysis of variance for the main effect of cohort, with a follow-up ANOVA and Tukey's Honest Significant Difference were used to discern which attributes differed between each cohort. Receiver operating characteristic curves were calculated, providing cutoff values between cohorts. Results: The national elite cohort was significantly taller than the state elite cohort (ES ¼ 0.94) and faster than the subelite athletes across 30 m (ES ¼ 0.79) and 20 m with a flying start (ES ¼ 0.77) (P < 0.05). The national elite cohort had a significantly higher level of intermittent endurance, compared to the state elite athletes who also performed better than the sub-elite cohort. The discrepancy between groups in the Yo-Yo Intermittent Recovery test level 1 was exemplified by the receiver operating characteristic with 94.1% of national elite players running further than 1980 m, while 95.7% of state elite and 100% of sub-elite players failed to reach this distance (ES ¼ 0.88-1.77). Conclusions: It is evident that anthropometrical and physical attributes differ between youth cohorts, particularly intermittent endurance. It is important to use this knowledge to enhance the current processes used to identify future talent for success in Australian soccer.

Via systematic review with narrative synthesis of findings, we aimed to document the ways by which researchers have defined, operationalized, and examined sleep variability among athletes. We identified studies in which scholars examined intraperson variability in sleep among athletes via a search of six databases (Web of Science, Embase, Medline, PsycINFO, CINHAL Plus, and ProQuest Dissertations and Theses Global) using a protocol that included keywords for the target outcome (sleep*), population (athlet* OR sport*), and outcome operationalization (variability OR variation OR “standard deviation” OR fluctuate OR fluctuation OR stability OR instability OR reactivity OR IIV OR intraindividual). We complemented this primary search with citation searching of eligible articles. Assessments of study quality captured eight core elements, namely aims/hypotheses, sample size justification, sample representativeness, number of days sleep assessed, measures of sleep and its correlates, missing data, and inferences and conclusions. From a total of 1209 potentially relevant papers, we identified 16 studies as meeting our eligibility criteria. Concept definitions of variability were notably absent from this work and where available were vague. Quantitative deviations from one's typical level of target sleep metrics reflected the essence by which all but one of the research teams operationalized sleep variability. We assessed the overall quality of empirical work as moderate in nature. We propose a working definition of sleep variability that can inform knowledge generation on the temporal, day‐to‐day dynamics of sleep functioning that is required for personalized interventions for optimizing sleep health.