Using an expert consensus-based approach, a netball video analysis consensus (NVAC) group of researchers and practitioners was formed to develop a video analysis framework of descriptors and definitions of physical, technical and contextual aspects for netball research. The framework aims to improve the consistency of language used within netball investigations. It also aims to guide injury mechanism reporting and identification of injury risk factors. The development of the framework involved a systematic review of the literature and a Delphi process. In conjunction with commercially used descriptors and definitions, 19 studies were used to create the initial framework of key descriptors and definitions in netball. In a two round Delphi method consensus, each expert rated their level of agreement with each of the descriptors and associated definition on a 5-point Likert scale (1-strongly disagree; 2-somewhat disagree; 3-neither agree nor disagree; 4-somewhat agree; 5-strongly agree). The median (IQR) rating of agreement was 5.0 (0.0), 5.0 (0.0) and 5.0 (0.0) for physical, technical and contextual aspects, respectively. The NVAC group recommends usage of the framework when conducting video analysis research in netball. The use of descriptors and definitions will be determined by the nature of the work and can be combined to incorporate further movements and actions used in netball. The framework can be linked with additional data, such as injury surveillance and microtechnology data.

Objectives. There is little understanding of the optimal technical performance characteristics associated with winning matches in netball and how these might vary between competition levels and between each quarter of the game. This study aims to identify and compare team technical performance characteristics between elite domestic and international-level matches and among different quarters. Design. A retrospective cohort study was used. Methods. Netball data sets were analysed, containing quarter-level and overall match statistics from the professional league in Australia and international tournaments (n = 1,296 records, 648 matches). Permutation resampling tests were used to compare technical variables between domestic and international matches, and between winning and losing teams at both the match and (non-cumulative) quarter levels. Machine learning methods were used to model match outcome and to rank technical characteristics. Results. Differences in team technical characteristics were observed between domestic and international matches, relating to penalties, and score-related actions. There are a variety of team technical characteristics that vary between winning and losing performances, and their importance varies slightly between quarters within a match. A K-Nearest Neighbour classifier explained match outcome with a high accuracy which demonstrates that there are enough data available in elite netball to apply machine learning methods for more complex analyses in the future. Conclusions. Team technical performance varies between winning and losing performances, at elite domestic and international levels. Minimising undesirable technical actions such as penalties and errors appears to be the most important difference between winning and losing teams.

Spatio-temporal data in sport is increasing rapidly, however suitable statistical methods for analysing this data are underdeveloped. The current study establishes the need for spatial statistical methods, propose a Bayesian hierarchical model as an appropriate method for comparing spatial variables, and test this model across three spatial scales. The need for spatial statistical methods was established through the identification of spatial autocorrelation. This necessitated the use of a Bayesian hierarchical model to test for an association between spatial ball movement entropy and spatial effectiveness. Posterior distribution results showed a generally positive association such that increases in entropy were associated with increases in effectiveness. The strength and confidence of the associations were impacted by the spatial scale, with the 6 x 6 grid showing the most conclusive evidence of a positive relationship; the 4 x 4 grid was mostly positive, however with a large variation; and finally, the basket-centric scale results were less conclusive. The results of the current study demonstrate the suitability of a Bayesian hierarchical model for testing for associations or differences between spatial variables. With the increase in spatial analyses in sport, this study presents an appropriate statistical method for dealing with complex problems associated with spatial analyses.

Netball is a newly professional women's sport, as such there has been little research conducted investigating performance analysis (PA) in elite netball. The aim of this study was to develop a model of the elite netball performance system to identify the complex relationships among key performance indicators. Eleven elite subject matter experts (SMEs) participated in workshops to produce a systems model of the netball match performance. The model was developed using the work domain analysis (WDA) method. A model of the netball match performance system was produced showing the interrelated objects, processes, functions, values, and purposes involved in elite level netball matches. The model identified the components of elite level netball performance and the interactions and relationships between them. The output of this research has identified novel PA measures including passing and possession measures, measures of cognitive performance, and measures related to physical activity. Netball is a complex sport, involving multiple dynamic and interrelated components. Consequently, there is an opportunity to develop holistic PA measures that focus on interacting components, as opposed to components in isolation.

Basketball strategy is often focused on how to use space on the court. However, very little research has investigated performance from a spatial perspective beyond the now ubiquitous shooting heat maps. The aim of this study was to quantify how effectively teams move the ball across the basketball court and identify the most commonly occurring sequences of ball movement in international women's basketball. The results of the spatial analysis characterised trends in team play from the women's 2016 Olympic basketball competition and demonstrated that overall, the right-hand side under the basket and the top-right 3-point area were the most-effective areas on the court. In general terms, the right-hand side of the court was more effective than the left, and the middle of the court was more effective than the wings. Of the teams included in the study, the United States of America demonstrated the greatest overall effectiveness. Finally, the most commonly occurring ball movement sequences were identified with five of the seven teams demonstrating the same pattern. The quantification of spatial effectiveness in the current study provides insight into the specific tendencies of different teams and the areas that lead to the most effective outcomes. Coaches can apply this information to devise game plans aimed at counteracting the specific tendencies of opposing teams.

Although systems thinking has been recently introduced as a means to model team performance, the most central and practically valuable question of this modeling tool is yet to be clearly addressed: how can the coaching team go from the level of team performance to the level of individual performance in order to select and evaluate players? In other words, if performance is a holistic phenomenon, how can the performance of individual players be conceptualized in relation to the whole? We appeal to the concepts of 'objective' and 'function' to show how team performance is linked to, and based on, the performance of individuals. We first describe team performance in relation to a set of objectives that are aimed to be achieved at different levels. Then we define the concept of function and break down this concept into three types, namely, positional, tactical, and interpreted function. We draw conceptual connections between different types of function and different levels of objectives. These connections show how each type of function links individual performance with team performance and how a team can be engineered as a coherent whole. We finish the paper by discussing some practical implications for coaches.

It is generally accepted that playing unpredictable basketball is advantageous, however this strategic assumption has not been adequately tested. The aim of this study was to describe unpredictability of in-play ball movement trajectories during a selection of women's international basketball games to determine the association, if any, between unpredictability and success in basketball. Ball movements were tracked for 60 international women's basketball games over a two-year period. Ball movements were broken into five-second play segments and the spatial distribution of the ball was tracked across the court. Shannon's entropy was then used to estimate the relative variability in ball movements. While no differences in entropy were observed between teams, the overall analysis revealed that entropy during large-deficit games (score differential of 10 points or more) was greater than that for small-deficit games and large-deficit wins showed greater entropy than large-deficit losses. Additionally, the entropy in the frontcourt (the scoring end for a given team) was significantly greater for wins compared to losses. This suggests that higher entropy may be associated with success in basketball, but more specifically, entropy in the frontcourt is potentially where it matters most.

Systems thinking frameworks have gained attention in both modelling athlete performance and injury prevention in sports medicine.1–3 We believe that these fields may contribute valuably and interdependently within a larger high performance system.1 The purpose of this editorial is to explain how ‘injury’ and ‘performance’ interact within a system-based framework3 and to provide three practical implications of an integrated performance system. The basic premise underpinning any systems model is that parts of a system are inter-related, and the objective of the whole system defines the function of each part. Therefore, the interaction between parts cannot be reduced to a number of linear cause and effect relations.1 The influence of a part on the outcome of the whole system depends on the state of the other parts. Any change to a part of the system can affect the objectives of the whole system as …

Systems thinking has been developed and used in many fields such as management, economics, and engineering in the past few decades. Although implicit elements of systems thinking may be found in some coaching biographies and autobiographies, a critical and explicit work on systems thinking that examines its principles and its relevance to sport sciences and coaching is yet to be developed. The aim of this Insight paper is to explore systems thinking and its potential for modelling and analysing team performance by (a) explaining how systems thinking is used in other fields, (b) highlighting the importance of conceptual analysis and critical thinking next to data collecting practices, and (c) contrasting systems thinking with the common approaches to modelling and analysing team performance.

PURPOSE:To determine differences in load/min (AU) between standards of netball match play.METHODS:Load/ min (AU) representing accumulated accelerations measured by triaxial accelerometers was recorded during matches of 2 higher- and 2 lower-standard teams (N = 32 players). Differences in load/min (AU) were compared within and between standards for playing position and periods of play. Differences were considered meaningful if there was >75% likelihood of exceeding a small (0.2) effect size.RESULTS:Mean (± SD) full-match load/min (AU) for the higher and lower standards were 9.96 ± 2.50 and 6.88 ± 1.88, respectively (100% likely lower). The higher standard had greater (mean 97% likely) load/min (AU) values in each position. The difference between 1st and 2nd halves' load/min (AU) was unclear at the higher standard, while lower-grade centers had a lower (-7.7% ± 10.8%, 81% likely) load/min (AU) in the 2nd half and in all quarters compared with the 1st. There was little intrastandard variation in individual vector contributions to load/min (AU); however, higher-standard players accumulated a greater proportion of the total in the vertical plane (mean 93% likely).CONCLUSIONS:Higher-standard players produced greater load/min (AU) than their lower-standard counterparts in all positions. Playing standard influenced the pattern of load/min (AU) accumulation across a match, and individual vector analysis suggests that different-standard players have dissimilar movement characteristics. Load/min (AU) appears to be a useful method for assessing activity profile in netball.

PURPOSE:To determine the impact of neuromuscular fatigue (NMF) assessed from variables obtained during a countermovement jump on exercise intensity measured with triaxial accelerometers (load per minute [LPM]) and the association between LPM and measures of running activity in elite Australian Football.METHODS:Seventeen elite Australian Football players performed the Yo-Yo Intermittent Recovery Test level 2 (Yo-Yo IR2) and provided a baseline measure of NMF (flight time:contraction time [FT:CT]) from a countermovement jump before the season. Weekly samples of FT:CT, coaches' rating of performance (votes), LPM, and percent contribution of the 3 vectors from the accelerometers in addition to high-speed-running meters per minute at >15 km/h and total distance relative to playing time (m/min) from matches were collected. Samples were divided into fatigued and nonfatigued groups based on reductions in FT:CT. Percent contributions of vectors to LPM were assessed to determine the likelihood of a meaningful difference between fatigued and nonfatigued groups. Pearson correlations were calculated to determine relationships between accelerometer vectors and running variables, votes, and Yo-Yo IR2 score.RESULTS:Fatigue reduced the contribution of the vertical vector by (mean ± 90% CI) -5.8% ± 6.1% (86% likely) and the number of practically important correlations.CONCLUSIONS:NMF affects the contribution of individual vectors to total LPM, with a likely tendency toward more running at low speed and less acceleration. Fatigue appears to limit the influence of the aerobic and anaerobic qualities assessed via the Yo-Yo IR2 test on LPM and seems implicated in pacing.

PURPOSE The purpose of this study was to determine if Yo-Yo Intermittent Recovery level 2 (Yo-Yo IR2) and the number of interchange rotations affected the match activity profile of elite Australian footballers. METHOD Fifteen elite Australian footballers completed the Yo-Yo IR2 before the beginning of the season and played across 22 matches in which match activity profiles were measured via microtechnology devices containing a global positioning system (GPS) and accelerometer. An interchange rotation was counted when a player left the field and was replaced with another player. Yo-Yo IR2 results were further split into high and low groups. RESULTS Players match speed decreased from 1st to 4th quarter, while average-speed (m/min: P = .05) and low-speed activity (LSA, <15 km/h) per minute (LSA m/min; P = .06) significantly decreased in the 2nd half. Yo-Yo IR2 influenced the amount of m/min, high-speed running (HSR, >15 km/h) per minute (HSR m/min) and accelerometer load/min throughout the entire match. The number of interchanges significantly influenced the HSR m/min and m/min throughout the match except in the 2nd quarter. Furthermore, the low Yo-Yo IR2 group had significantly less LSA m/min in the 4th quarter than the high Yo-Yo IR2 group (92.2 vs 96.7 m/min, P = .06). CONCLUSIONS Both the Yo-Yo IR2 and number of interchanges contribute to m/min and HSR m/min produced by elite Australian footballers, affecting their match activity. However, while it appears that improved Yo-Yo IR2 performance prevents reductions in LSA m/min during a match, higher-speed activities (HSR m/min) and overall physical activity (m/min and load/min) are still reduced in the 4th quarter compared with the 1st quarter.

This study aimed to quantify the influence of neuromuscular fatigue (NMF) via flight time to contraction time ratio (FT:CT) obtained from a countermovement jump (CMJ) on the relationships between yo-yo intermittent recovery (level 2) test (yo-yo IR2), match exercise intensity (high-intensity running [HIR] m·min(-1) and Load·min(-1)) and Australian football (AF) performance. Thirty-seven data sets were collected from 17 different players across 22 elite AF matches. Each data set comprised an athlete's yo-yo IR2 score before the start of the season, match exercise intensity via global positioning system and on-field performance rated by coaches' votes and number of ball disposals. Each data set was categorized as normal (>92% baseline FT:CT, n = 20) or fatigued (<92% baseline FT:CT, n = 17) from a single CMJ performed 96 hours after the previous match. Moderation-mediation analysis was completed with yo-yo IR2 (independent variable), match exercise intensity (mediator), and AF performance (dependent variable) with NMF status as the conditional variable. Isolated interactions between variables were analyzed by Pearson's correlation and effect size statistics. The Yo-yo IR2 score showed an indirect influence on the number of ball disposals via HIR m·min(-1) regardless of NMF status (normal FT:CT indirect effect = 0.019, p < 0.1, reduced FT:CT indirect effect = 0.022, p < 0.1). However, the yo-yo IR2 score only influenced coaches' votes via Load·min(-1) in the nonfatigued state (normal: FT:CT indirect effect = 0.007, p <0.1, reduced: FT:CT indirect effect = -0.001, p > 0.1). In isolation, NMF status also reduces relationships between yo-yo IR2 and load·min(-1), yo-yo IR2 and coaches votes, Load·min(-1) and coaches' votes (Δr > 0.1). Routinely testing yo-yo IR2 capacity, NMF via FT:CT and monitoring Load·min(-1) in conjunction with HIR m·min(-1) as exercise intensity measures in elite AF is recommended.

The aim of this study was to verify if yo-yo intermittent recovery test (level 2) (yo-yo IR2) score is linked to Australian football (AF) performance through match exercise intensity. Six week prospective study design. Twenty-one data sets were recorded from nine individual players that completed the yo-yo IR2, and played an Australian Football League match in the first five rounds of the 2010 season wearing a global positioning system (GPS) unit. Simple mediation modelling was used to analyse the inter-relationship between yo-yo IR2 score, match exercise intensity and AF performance. Playing position and experience were also incorporated into the model to identify conditional affects. A significant direct relationship was observed between yo-yo IR2 and number of ball disposals (p<0.1) and a significant indirect relationship was observed between yo-yo IR2 and number of ball disposals through distance travelled at high intensity (HIR mmin−1) (p<0.1). Moderation analysis showed that playing position affected the relationship between of yo-yo IR2 and HIR mmin−1 (p<0.1) and HIR mmin−1 and total ball disposals (p<0.1). Playing experience also significantly affected the relationship between HIR mmin−1 and total ball disposals. This study is the first to identify the effects of yo-yo IR2 on total ball disposals through HIR mmin−1 performed during AF matches, and that playing position and playing experience affect these interactions.

Hunter, JR, O'Brien, BJ, Mooney, MG, Berry, J, Young, WB, and Down, N. Repeated sprint training improves intermittent peak running speed in team-sport athletes. J Strength Cond Res 25(5): 1318-1325, 2011-The aim of this study was to compare the effect of 2 repeated sprint training interventions on an intermittent peak running speed (IPRS) test designed for Australian Rules football. The test required participants to perform 10 × 10-m maximal efforts on an 80-m course every 25 seconds, for each of which the mean peak speed (kilometers per hour) was recorded to determine IPRS. The training interventions were performed twice weekly for 4 weeks immediately before regular football training. In the constant volume intervention (CVol), sprint repetition number remained at 10 (n = 9), and in the linear increase in volume (LIVol) intervention, repetition number increased linearly each week by 2 repetitions (n = 12). Intermittent peak running speed, 300-m shuttle test performance, and peak running speed were assessed before and upon completion of training. All measures were compared to a control group (CON; n = 8) in which players completed regular football training exclusively. Intermittent peak running speed performance in CVol and LIVol improved significantly (p < 0.01) by 5.2 and 3.8%, respectively, with no change in IPRS for CON. There were no differences in IPRS changes between CVol and LIVol. Additionally, peak running speed improved significantly (p < 0.01) by 5.1% for CVol, whereas 300-m shuttle performance improved significantly (p < 0.01) by 2.6% for LIVol only. Intermittent peak running speed, 300-m shuttle performance and peak running speed were improved after 4 weeks of training; however, progressively increasing sprint repetition number had no greater advantage on IPRS adaptation. Additionally, exclusive regular football training over a 4-week period is unlikely to improve IPRS, peak running speed, or 300-m shuttle performance.

Mooney, MG, Hunter, JR, O'Brien, BJ, Berry, JT, and Young, WB. Reliability and validity of a novel intermittent peak running speed test for Australian football. J Strength Cond Res 25(4): 973-979, 2011-Australian football requires frequent intermittent sprinting close to peak running speed. However, tests assessing the capability to maintain intermittent peak running speed are not reported in scientific literature. Therefore, our objective is to report the reliability and validity of a novel intermittent peak running speed test. The intermittent peak running speed test required footballers to perform 10 repetitions on 25-second intervals. Each repetition required 15-m jogging, 20-m acceleration to peak speed, 10 m to sustain peak speed, 20-m deceleration, and finally a 15-m jog. Intermittent peak running speed was determined by portable global positioning system. To assess reliability, 26 footballers performed the intermittent peak running speed test on 2 occasions 3-5 days apart. Our results revealed that average peak speed had a coefficient of variation of 2.2% and an intraclass correlation of 0.91. To assess construct validity, average peak speed was compared between elite, sub-elite, and regional footballers. The average peak speed of the elite footballers (28.6 ± 1.7 km·h−1) was higher than that of the sub-elite (27.4 ± 1.7 km·h −1) and regional (27 ± 1.9 km·h−1) competitors (p < 0.05). Our study revealed that the intermittent peak running speed test possesses acceptable reliability and distinguishes between elite and sub-elite footballers.