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Purpose: To investigate differences in athletes’ knowledge, beliefs, and training practices during COVID-19 lockdowns with reference to sport classification and sex. This work extends an initial descriptive evaluation focusing on athlete classification. Methods: Athletes (12,526; 66% male; 142 countries) completed an online survey (May–July 2020) assessing knowledge, beliefs, and practices toward training. Sports were classified as team sports (45%), endurance (20%), power/technical (10%), combat (9%), aquatic (6%), recreational (4%), racquet (3%), precision (2%), parasports (1%), and others (1%). Further analysis by sex was performed. Results: During lockdown, athletes practiced body-weight-based exercises routinely (67% females and 64% males), ranging from 50% (precision) to 78% (parasports). More sport-specific technical skills were performed in combat, parasports, and precision (∼50%) than other sports (∼35%). Most athletes (range: 50% [parasports] to 75% [endurance]) performed cardiorespiratory training (trivial sex differences). Compared to prelockdown, perceived training intensity was reduced by 29% to 41%, depending on sport (largest decline: ∼38% in team sports, unaffected by sex). Some athletes (range: 7%–49%) maintained their training intensity for strength, endurance, speed, plyometric, change-of-direction, and technical training. Athletes who previously trained ≥5 sessions per week reduced their volume (range: 18%–28%) during lockdown. The proportion of athletes (81%) training ≥60 min/session reduced by 31% to 43% during lockdown. Males and females had comparable moderate levels of training knowledge (56% vs 58%) and beliefs/attitudes (54% vs 56%). Conclusions: Changes in athletes’ training practices were sport-specific, with few or no sex differences. Team-based sports were generally more susceptible to changes than individual sports. Policy makers should provide athletes with specific training arrangements and educational resources to facilitate remote and/or home-based training during lockdown-type events.

Analysis of the underlying tactics and teamwork in women's football is rare and it is unknown how professional women's teams cooperate to be successful. The aim of this study was to investigate teamwork using network analysis while comparing match-outcome, match-type, ladder halves and tournament phases, to determine whether teamwork is related to success. Ball transfer data in 694 matches from the 2015, 2016 and 2017/18 Football Association Women's Super League (FA WSL) seasons; 2016–2018 National Women's Football League (NWSL) seasons; 2013 and 2017 European Cups; and 2011 and 2013 World Cups were analysed. The network metrics: edge density, transitivity, mean distance, out degree centrality, closeness centrality, betweenness centrality and eigenvector centrality were calculated. Success was categorised in match outcomes, ladder halves, tournament phases and ladder positions. It was found that successful professional women's football teams are highly connected (p = 0.006), and the distribution of ball possession is centralised (p = 0.001). There is a tendency for key players to send out a high number of passes, but there is no dependency on these key players for the total ball flow within a team, which is a characteristic that may be unique to women's football. Differences in teamwork exist between single matches and full seasons or tournaments, with successful teams having more effective ball movement and successful passes over the course of a season or tournament (p < 0.001). Moreover, successful league teams have more players with connecting roles than tournament teams and match tactics should be adapted to this.

Over a 15-day period, that included an eight-day field trial, the aims of this study were to (1) quantify the physical workload, sleep and subjective well-being of soldiers in training; (2a) Explore relationships between workload and well-being, and (2b) sleep and well-being; (3) Explore relationships between workload, sleep, and well-being. Methods: Sixty-two Combat Engineer trainees (59 male, 3 female; age: 25.2 ± 7.2 years) wore an ActiGraph GT9X to monitor daily energy expenditure, physical activity, and sleep. Rating of perceived exertion (RPE), sleep quality, and fatigue were measured daily, subjective well-being was reported days 1, 5, 9, 13 and 15. Multi-level models were used for the analysis. Results: Well-being was affected by a combination of variables including workload, subjective sleep quality, sleep duration, and sleep efficiency. RPE and subjective sleep quality were consistently significant parameters within the models of best fit. Conclusions: Perceptions of well-being were lower during the field training when physical workload increased, and sleep decreased. Energy expenditure was comparatively low, while daily sleep duration was consistent with field training literature. Subjective assessments of workload and sleep quality were consistently effective in explaining variations in well-being and represent an efficient approach to monitor training status of personnel.

Peek, RJ, Carey, DL, Middleton, KJ, Gastin, PB, and Clarke, AC. Association between position-specific impact and movement characteristics of professional rugby union players during game play. J Strength Cond Res 37(1): 161-166, 2023-The aim of this study was to understand the association between impact and movement characteristics during whole game and peak 1- to 10-minute rolling windows in professional rugby union. Maximal impact (impacts·min-1) and corresponding running (m·min-1) characteristics as well as maximal running (m·min-1) and corresponding impact (impacts·min-1) characteristics were obtained for 160 athletes from 4 teams across the 2018 and 2019 Super Rugby seasons. A linear mixed-effects model reported a positive association between whole-game running and impacts, where greater impact characteristics corresponded with greater running characteristics. The average 1-minute peak running characteristics (150-160 m·min-1) typically occurred when no impacts occurred. The average 1-minute peak impact characteristics (4-6 impacts·min-1) corresponded with an average relative distance of 90-100 m·min-1. Worst case scenario observed impact characteristics as large as 15 impacts·min-1 with a corresponding relative distance of 140 m·min-1. When training for peak period characteristics, running may be completed in isolation; however, peak impacts often occur in conjunction with moderate to high running movements. Given running and impact characteristics can appear concurrently within game play, this highlights the need to train them accordingly. As such, when prescribing training drills to replicate the peak characteristics in rugby union, consideration should be taken for both running and impact characteristics.

Australian aerial skiers are recruited from other acrobatic sports through a nationally coordinated transition program. However, there is limited understanding of how to train transferring athletes for both optimal performance and injury prevention. The aim of this study was to test whether an athletic profile could be used to identify which skills that gymnasts possess are more and less likely to transfer to aerial skiing based on similarities and differences in the athletes’ physical attributes. Six elite female aerial skiers and five state- and national-level female gymnasts completed a test protocol involving drop-landing, countermovement jump, isokinetic strength of the knee flexors and extensors, flexibility and anthropometrical profile. Aerial skiers and gymnasts had an anthropometrical profile ideal for acrobatic efficiency, but gymnasts had smaller thigh girth and greater knee range of motion. These results suggest that gymnasts are more likely to succeed in transferring to aerial skiing's acrobatic skills than skiing and landing skills. We suggest that gymnasts transferring to aerial skiing undertake pretransfer training designed to improve hamstring isometric strength and dynamic stability of the knee to reduce the risk of knee injury and improve their ability to maintain a crouched position.

This study quantifies the maximum number of impacts and peak running demands during 1– to 10– minute rolling window periods in elite rugby union matches using a multi-team dataset (n = 2232 player-games). Maximum values for impacts (impacts·min−1) and running (m·min−1) were calculated for 161 athletes from four teams across the 2018 and 2019 Super Rugby seasons. The effect of window duration and playing position on peak impact and running demands were estimated using linear mixed effect models and prediction intervals. The peak impact and running demands for a 1-min period were 4.5 – 5.5 impacts·min−1 and 150 – 180 m·min−1, depending on playing position. While small variations in mean impact and running movements could be observed by position, the large prediction interval and individual player variation meant that there was no practically meaningful difference by position. As such, when prescribing training drills to replicate the peak demands in rugby union, impact and running movements of players can be similar, regardless of position. Using a prediction interval allows us to identify the range where the demands in a future game may fall, and are beneficial to use when also trying to prepare players for the demands of rugby union.

Background: Elite junior Australian football players experience high training loads across levels of competition and training. This, in conjunction with impaired wellness, can predispose athletes to injury. Hypothesis: Elite junior Australian football players exposed to high loads with poor wellness are more likely to be at risk of injury than those with improved wellness. Study Design: Longitudinal prospective cohort study. Level of Evidence: Level 3. Methods: Data were collected and analyzed from 280 players across the 2014 season. Internal load was measured via session rating of perceived exertion. Player wellness was reported according to ratings of sleep quality, fatigue, soreness, stress, and mood. Week- and month-based training load measures were calculated, representing a combination of absolute and relative load variables. Principal component analysis factor loadings, based on 17 load and wellness variables, were used to calculate summed variable covariates. Injury was defined as “any injury leading to a missed training session or competitive match.” Associations between covariates and injury risk (yes/no) were determined via logistic generalized estimating equations. Results: A significant interaction term between load and wellness on injury was found [odds ratio (OR) 0.76; 95% CI 0.62-0.92; P < 0.01), indicating that wellness acts as a “dimmer switch” of load on injury. Further, there was evidence of moderated mediation (OR 0.71; 95% CI 0.57-0.87; P < 0.01). When wellness was low, injury risk started to increase substantially at a 1-week load of 3250 au. Conclusions: Subjective measures of training load are associated with injury risk through a nonlinear relationship. This relationship is further influenced by player wellness, which can amplify the risk of injury. There is evidence that higher stress is linked with injury and that soreness and sleep mediate any stress-injury relationship. Clinical Relevance: Coaching efforts to manage training load and player adaptive responses, including wellness, may reduce the risk of injury, with stress, soreness, and sleep particularly relevant at this level.

Purpose: To investigate the association between player wellness and injury in elite junior Australian football players over 1 competitive season. Methods: Prospective cohort study. Elite junior Australian football players (N = 196, average age = 17.7 y, range = 16–18 y) were recruited in the under-18 state league competition in Victoria, Australia. They recorded their wellness (sleep, fatigue, soreness, stress, and mood) according to a 5-point Likert scale 3 times weekly, with injuries (missed match/training session) entered into an online sport-injury surveillance system. A logistic generalized estimating equation was used to examine the association (expressed as odds ratio [OR]) between wellness and injury (yes/no). Results: Soreness was associated with injury at each time point across the week, with the strongest association evident for soreness reported 6 d postmatch (OR = 1.30; 95% confidence interval [CI], 1.17–1.44; P P = .038). Mood reported in the middle of the week (3 d postmatch) was associated with injury (OR = 0.87; 95% CI, 0.78–0.97; P = .014), as was fatigue (OR = 1.10; 95% CI, 1.00–1.22; P = .044). Conclusions: This study demonstrates key associations between wellness and injury in elite junior Australian football, specifically soreness, stress, fatigue, and mood. Monitoring strategies help identify injury-risk profiles, which can help decision makers (coaches or medical staff) intervene when relevant to reduce injury risk.

This study examined the perceived talent development environment (TDE) of youth track and field athletes within the Caribbean region and explores the relationship with their subjective wellbeing and burnout levels. A sample of 400 Caribbean youth track and field athletes (male = 198, and female = 202; age 13-20 years) participated in this cross-sectional study. Athletes responded to a survey which included the Talent Development Environment Questionnaire (TDEQ-5), the World Health Organization Wellbeing Index (WHO-5) and the Athlete Burnout Questionnaire (ABQ). Structural equation modelling showed that a positive and supportive TDE was associated with better athlete wellbeing and lower athlete burnout. Moreover, gender and hours of training moderated relationships of athletes' perceptions of their development environment with wellbeing and burnout levels respectively. Furthermore, there was a positive association between the support network and athlete wellbeing, while holistic quality preparation was negatively associated with athlete burnout. Development environments perceived as supportive and geared towards a holistic athlete-centred approach were associated with better athlete wellbeing and lower burnout.

The study aim was to investigate ball movement patterns using network analysis techniques, to compare between successful and unsuccessful outcomes and teams in the Australian Football League (AFL). This analysis focused on possession chains starting from a kick-in (n = 1,720), drawn from all games played in the 2015 AFL Premiership season (18 teams, 206 games). Player interactions were quantified using four network metrics: cluster coefficient, degree centrality, network density, and entropy. Three-way ANOVA with Tukey post hoc and ω

Purpose: To investigate the association between training and match loads and injury in elite junior Australian football players over 1 competitive season. Methods: Elite junior Australian football players (n = 290, age 17.7 [0.3] y, range 16–18 y) were recruited from the under-18 state league competition in Victoria to report load and injury information. One-week load (session rating of perceived exertion multiplied by duration) and all time-loss injuries were reported using an online sport-injury surveillance system. Absolute load measures (weekly sums) enabled the calculation of relative measures such as the acute:chronic workload ratio. Load measures were modeled against injury outcome (yes/no) using a generalized estimating equation approach, with a 1-wk lag for injury. Results: Low (4650 au) 1-wk loads were associated with significantly higher risk of injury. Furthermore, low (850 au) session loads were associated with a higher risk of injury. High strain values (>13,000) were associated with up to a 5-fold increase in the odds of injury. There was a relatively flat-line association between the acute:chronic workload ratio and injury. Conclusions: This study is the first investigation of elite junior athletes demonstrating linear and nonlinear relationships between absolute and relative load measures and injury. Coaches should focus player loads on, or at least close to, the point at which injury risk starts to increase again (2214 au for 1-wk load and 458 au for session load) and use evidence-based strategies across the week and month to help reduce the risk of injury.

The aim of this study was to assess if tactical and technical performance indicators (PIs) could be used in combination to model match outcomes in Australian Football (AF). A database of 101 technical PIs and 14 tactical PIs from every match in the 2009-2016 Australian Football League (AFL) seasons was merged. Two outcome measures Win-loss and Score margin were used as dependent variables. The top 45 ranked technical and tactical PIs from a feature selection process were used to model match outcome using decision tree and Generalised Linear Models (GLMs). Of the top 45 selected features, this included seven tactical PIs. The Win-loss-based Decision tree model achieved a classification accuracy of 89.0% and GLM 93.2%. A Score margin-based GLM achieved a root mean squared error (RMSE) of 6.9 points. A combined approach to the classification of match outcomes provided no improvement in model accuracy compared with previous literature. However, this study has established the relative importance of technical and tactical measures of performance in relation to successful team performance in AF.

Team sports can be viewed as dynamical systems unfolding in time and thus require tools and approaches congruent to the analysis of dynamical systems. The analysis of the pattern-forming dynamics of player interactions can uncover the clues to underlying tactical behaviour. This study aims to propose quantitative measures of a team's performance derived only using player interactions. Concretely, we segment the data into events ending with a goal attempt, that is, “$Shot$”. Using the acquired sequences of events, we develop a coarse-grain activity model representing a player-to-player interaction network. We derive measures based on information theory and total interaction activity, to demonstrate an association with an attempt to score. In addition, we developed a novel machine learning approach to predict the likelihood of a team making an attempt to score during a segment of the match. Our developed prediction models showed an overall accuracy of 75.2% in predicting the correct segmental outcome from 13 matches in our dataset. The overall predicted winner of a match correlated with the true match outcome in 66.6% of the matches that ended in a result. Furthermore, the algorithm was evaluated on the largest available open collection of soccer logs. The algorithm showed an accuracy of 0.84 in the classification of the 42, 860 segments from 1, 941 matches and correctly predicted the match outcome in 81.9% of matches that ended in a result. The proposed measures of performance offer an insight into the underlying performance characteristics.

Purpose: Assess and describe the physical demands and changes in subjective wellbeing of recruits completing the 12 week Australian Army Basic Military Training (BMT) course. Methods: Thirty-five recruits (24.8 ± 6.8 y; 177.4 ± 10.1 cm, 75.6 ± 14.7 kg) consented to daily activity monitoring and weekly measures of subjective wellbeing (Multi-component Training Distress Scale, MTDS). The physical demands of training were assessed via wrist worn activity monitors (Actigraph GT9X accelerometer). Physical fitness changes were assessed by push-ups, sit-ups and multi-stage shuttle run in weeks 2 and 8. Results: All objective and subjective measures significantly changed (p < 0.05) across the 12 week BMT course. In parallel, there was a significant improvement in measures of physical fitness from weeks 2 to 8 (p < 0.001). The greatest disturbance to subjective wellbeing occurred during week 10, which was a period of field training. Weeks 6 and 12 provided opportunities for recovery as reflected by improved wellbeing. Conclusions: The physical demands of training varied across the Australian Army 12 week BMT course and reflected the intended periodization of workload and recovery. Physical fitness improved from week 2 to 8, indicating a positive training response to BMT. Consistent with findings in sport, wellbeing measures were sensitive to fluctuations in training stress and appear to have utility for individual management of personnel in the military training environment.

This qualitative investigation sought to explore through a socio-cultural lens the perceived early training and competition environment, and support network of world-class Caribbean track and field athletes and the influence on their sport engagement and progression during early childhood and adolescence. Sixteen world-class track and field athletes (8 males and 8 females; M age = 29, SD = 5 years) from 6 English-speaking Caribbean islands took part in semi-structured interviews. A thematic analysis was performed on the transcribed data. Three superordinate themes were identified as key factors that influenced the early sporting development of world-class Caribbean athletes: (1) conducive sporting environment, (2) functional social support network, and (3) key organizational input. Findings revealed that perceived high levels of deliberate play activity in childhood (6 – 12 years) and an intense track and field competition culture in adolescence (13 – 20 years) were conducive to the continued engagement and progression of world-class Caribbean track and field athletes at the junior level. Furthermore, world-class athletes perceived themselves to be positively influenced by the support received from their immediate social support network and key organizations during this period. This study showed that a conducive sporting environment coupled with optimal social and organizational support may have encouraged world-class Caribbean athletes to remain engaged in track and field and to successfully progress within the sport at the junior level. Findings shed light on the sporting culture at the junior level within the Caribbean region and provide insight into key environmental factors that can influence and foster the development of future World Champions and Olympians.

This study identifies the distribution (as a percentage of the positional peak) and occurrence (within a half) of 1-min movement characteristics (m·minWithin 95 rugby union games, players wore global positioning system (GPS) devices (n = 1422 player-game files, 160 athletes). One-minute rolling-window averages were calculated with the maximum and mean value being recorded for each dependent variable; relative distance (m·minThe distribution of these variables accumulated in 10% increments of the peak were determined as was their occurrence throughout a playing half for each positional group.The greatest distribution of game play (%) was observed at ~30-39% of the positional peak for movement and with no impacts. The greatest occurrence of maximum positional peak 1-min movement periods occurred at the beginning of each half and declined as the half went on. Peak impact characteristics, however, were found to be more sporadic across a half.These findings suggest that the distribution of positional peak 1-min movement (80-100%+) and peak impacts (4+) were5% of overall game play, and observed2% of total occurrences. This highlights that practitioners should alter training drills by time, volume, and intensity in order to replicate the peak intensities of game play.

Purpose: Information from the Pittsburgh Sleep Quality Index (PSQI) and Athlete Sleep Behavior Questionnaire (ASBQ) provide the ability to identify the sleep disturbances experienced by athletes and their associated athlete-specific challenges that cause these disturbances. However, determining the appropriate support strategy to optimize the sleep habits and characteristics of large groups of athletes can be time-consuming and resource-intensive. The purpose of this study was to characterize the sleep profiles of elite athletes to optimize sleep-support strategies and present a novel R package, AthSlpBehaviouR, to aid practitioners with athlete sleep monitoring and support efforts. Methods: PSQI and ASBQ data were collected from a cohort of 412 elite athletes across 27 sports through an electronic survey. A k-means cluster analysis was employed to characterize the unique sleep-characteristic typologies based on PSQI and ASBQ component scores. Results: Three unique clusters were identified and qualitatively labeled based on the z scores of the PSQI components and ASBQ components: cluster 1, “high-priority; poor overall sleep characteristics + behavioral-focused support”; cluster 2, “medium-priority, sleep disturbances + routine/environment-focused support”; and cluster 3, “low-priority; acceptable sleep characteristics + general support.” Conclusions: The findings of this study highlight the practical utility of an unsupervised learning approach to perform clustering on questionnaire data to inform athlete sleep-support recommendations. Practitioners can consider using the AthSlpBehaviouR package to adopt a similar approach in athlete sleep screening and support provision.

Objective Studies in basic military training (BMT) examining sleep are largely cross-sectional, and do not investigate relationships between sleep, stress, recovery and fatigue. The aims of this study were to (1a) quantify changes in recruits’ sleep quantity and quality over 12 weeks of BMT; (1b) quantify changes in recruits’ perceptions of stress, fatigue and recovery over BMT; and (2) explore relationships between sleep, and perceptions of stress, fatigue and recovery. Methods 45 recruits (37 male; 8 female, age: 25.2 ± 7.2 years, height: 176.2 ± 10.0 cm, mass: 76.8 ± 15.0 kg) wore ActiGraph GT9X’s for 12 weeks of BMT, collecting sleep duration, efficiency and awakenings. Subjective sleep quality, fatigue were measured daily, with stress and recovery measured weekly. Multi-level models assessed relationships between sleep, and stress, recovery, and fatigue. Results Objective daily means for sleep duration were 6.3 h (± 1.2 h) and 85.6% (± 5.5%) for sleep efficiency. Main effects were detected for all mean weekly values (p 0.05). Sleep quality showed the strongest relationships with stress, recovery and fatigue. The best model to explain relationships between, stress, recovery and fatigue, included sleep quality, sleep duration, sleep efficiency and awakenings. Conclusions The reported mean sleep duration of 6.3 h per night may negatively impact training outcomes across BMT. Combining both subjective and objective measures of sleep best explained relationships between sleep metrics stress, fatigue and recovery. Perceived sleep quality was most strongly related to change in stress, recovery, or post-sleep fatigue.

Anecdotal reports within the Caribbean track and field fraternity have revealed that there is a high level of athlete dropout from competitive sport at the junior-elite level, and a poor transition to senior-elite status. Consequently, this qualitative investigation explored the key motives that may have contributed toward the unsuccessful transitions and ensuing dropout of Caribbean track and field athletes during the junior to senior transition period. Eleven former junior-elite track and field athletes (four males, seven females; Mage= 29, SD ± 4.2 years) from four English-speaking Caribbean islands participated in semi-structured interviews. Following an inductive and deductive thematic analysis, four higher order themes were identified: (1) “there's not enough support”; (2) “felt pressure to make sure I committed”; (3) “it's always competitive here”; and (4) “battle with the injuries.” For these former junior-elite Caribbean athletes, the decision on whether to continue within the sport was influenced by a combination of factors, although inadequate financial and organizational support had the most bearing on athletes' decision to drop out during the crucial transition years. Implications for consideration by key stakeholders and policymakers within the region are discussed.

Mathematical models that explain match outcome, based on the value of technical performance indicators (PIs), can be used to identify the most important aspects of technical performance in team field-sports. The purpose of this study was to evaluate several methodological opportunities, to enhance the accuracy of this type of modelling. Specifically, we evaluated the potential benefits of 1) modelling match outcome using an increased number of seasons and PIs compared with previous reports, 2) how to identify eras where technical performance characteristics were stable and 3) the application of a novel feature selection method. Ninety-one PIs across sixteen Australian Football (AF) League seasons were analysed. Change-point and Segmented Regression analyses were used to identify eras and they produced similar but non-identical outcomes. A feature selection ensemble method identified the most valuable 45 PIs for modelling. The use of a larger number of seasons for model development lead to improvement in the classification accuracy of the models, compared with previous studies (88.8 vs 78.9%). This study demonstrates the potential benefits of large databases when creating models of match outcome and the pitfalls of determining whether there are eras in a longitudinal database.

Purpose: To investigate associations between load (training and competition) and wellness in elite junior Australian Football players across 1 competitive season. Methods: A prospective cohort study was conducted during the 2014 playing season in 562 players from 9 teams. Players recorded their training and match intensities according to the session-rating-of-perceived-exertion (sRPE) method. Based on sRPE player loads, a number of load variables were quantified, including cumulative load and the change in load across different periods of time (including the acute-to-chronic load ratio). Wellness was quantified using a wellness index including sleep, fatigue, soreness, stress, and mood on a Likert scale from 1 to 5. Results: Players spent an average of 85 (21) min in each match and 65 (31) min per training session. Average match loads were 637 (232) arbitrary units, and average training loads were 352 (233) arbitrary units. Over the 24 wk of the 2014 season, overall wellness had a significant linear negative association with 1-wk load (B = −0.152; 95% confidence interval, −0.261 to −0.043; P = .006) and an inverse U-curve relationship with session load (B = −0.078; 95% confidence interval, 0.143 to 0.014; P = .018). Mood, stress, and soreness were all found to have associations with load. Conclusions: This study demonstrates that load (within a session and across the week) is important in managing the wellness of elite junior Australian Football players. Quantifying loads and wellness at this level will help optimize player management and has the potential to reduce the risk of adverse events such as injury.

Objectives To identify novel insights about performance in Australian Football (AF), by modelling the relationships between player actions and match outcomes. This study extends and improves on previous studies by utilising a wider range of performance indicators (PIs) and a longer time frame for the development of predictive models. Design Observational. Methods Ninety-one team PIs from the 2001 to 2016 Australian Football League seasons were used as independent variables. The categorical Win–Loss and continuous Score Margin match outcome measures were used as dependent variables. Decision tree and Generalised Linear Models were created to describe the relationships between the values of the PIs and match outcome. Results Decision tree models predicted Win–Loss and Score Margin with up to 88.9% and 70.3% accuracy, respectively. The Generalised Linear Models predicted Score Margin to within 6.8 points (RMSE) and Win–Loss with up to 95.1% accuracy. The PIs that are most predictive of match outcome include; Turnovers Forced score, Inside 50 s per shot, Metres Gained and Time in Possession, all in their relative (to opposition) form. The decision trees illustrate how combinations of the values of these PIs are associated with match outcome, and they indicate target values for these PIs. Conclusions This work used a wider range of PIs and more historical data than previous reports and consequently demonstrated higher prediction accuracies and additional insights about important indicators of performance. The methods used in this work can be implemented by other sport analysts to generate further insights that support the strategic decision-making processes of coaches.

Existing physiological control fatigue models propose that there may be a regulator in the central nervous system which modulates our daily physical activity. Within limits, this regulator ensures physical activity is completed without physiological system failure through interactive communications between the peripheral systems and the central systems. The ability of the central nervous system to regulate exercise is vital to optimise sport performance when severe intensity exercise might be required for prolonged or frequent periods. Based on mathematical models, this investigation explores the complex relationship between some of the mechanisms controlling physical activity and behaviour. In order to analyse the system control mechanisms, heart rate, volume of oxygen consumption and power output were measured for a well-trained male cyclist. Using power spectrum analysis, fractal analysis and continuous wavelet transforms, we show that the system control mechanisms regulating physiological systems, have distinct complexity. Moreover, the potential central controller uses specific frequency bands simultaneously to control and communicate with the various physiological systems. We show that pacing trials are regulated by different physiological systems.

Ensuring a balance between training demands and recovery during basic military training (BMT) is necessary for avoiding maladaptive training responses (e.g., illness or injury). These can lead to delays in training completion and to training attrition. Previously identified predictors of injury and attrition during BMT include demographic and performance data, which are typically collected at a single time point. The aim of this study was to determine individual risk factors for injury and training delays from a suite of measures collected across BMT. A total of 46 male and female recruits undertaking the 12-week Australian Army BMT course consented to this study. Injury, illness, attrition, and demographic data were collected across BMT. Objective measures included salivary cortisol and testosterone, step counts, cardiorespiratory fitness, and muscular endurance. Perceptions of well-being, recovery, workload, fatigue, and sleep were assessed with questionnaires. Baseline and mean scores across BMT were evaluated as predictors of injury and attrition using generalized linear regressions, while repeated-measures ANOVA was used for the group comparisons. From the 46 recruits, 36 recruits completed BMT on time; 10 were delayed in completion or discharged. Multiple risk factors for injury during BMT included higher subjective ratings of training load, fatigue, and stress, lower sleep quality, and higher cortisol concentrations. Higher ratings of depression, anxiety, and stress, and more injuries were associated with a higher risk of delayed completion. Higher concentrations of testosterone and higher levels of fitness upon entry to BMT were associated with reduced risk of injury and delayed completion of BMT. Ongoing monitoring with a suite of easily administered measures may have utility in forewarning risk of training maladaptation in recruits and may complement strategies to address previously identified demographic and performance-based risk factors to mitigate injury, training delays, and attrition.

Background: Identifying key variables that predict sleep quality in youth athletes allows practitioners to monitor the most parsimonious set of variables that can improve athlete buy-in and compliance for athlete self-report measurement. Translating these findings into a decision-making tool could facilitate practitioner willingness to monitor sleep in athletes. Hypothesis: Key predictor variables, identified by feature reduction techniques, will lead to higher predictive accuracy in determining youth athletes with poor sleep quality. Study Design: Cross-sectional study. Level of Evidence: Level 3. Methods: A group (N = 115) of elite youth athletes completed questionnaires consisting of the Pittsburgh Sleep Quality Index and questions on sport participation, training, sleep environment, and sleep hygiene habits. A least absolute shrinkage and selection operator regression model was used for feature reduction and to select factors to train a feature-reduced sleep quality classification model. These were compared with a classification model utilizing the full feature set. Results: Sport type, training before 8 am, training hours per week, presleep computer usage, presleep texting or calling, prebedtime reading, and during-sleep time checks on digital devices were identified as variables of greatest influence on sleep quality and used for the reduced feature set modeling. The reduced feature set model performed better (area under the curve, 0.80; sensitivity, 0.57; specificity, 0.80) than the full feature set models in classifying youth athlete sleep quality. Conclusion: The findings of our study highlight that sleep quality of elite youth athletes is best predicted by specific sport participation, training, and sleep hygiene habits. Clinical Relevance: Education and interventions around the training and sleep hygiene factors that were identified to most influence the sleep quality of youth athletes could be prioritized to optimize their sleep characteristics. The developed sleep quality nomogram may be useful as a decision-making tool to improve sleep monitoring practice among practitioners.

Purpose: Elite athletes experience chronic sleep insufficiency due to training and competition schedules. However, there is little research on sleep and caffeine use of elite youth athletes and a need for a more nuanced understanding of their sleep difficulties. This study aimed to (1) examine the differences in sleep characteristics of elite youth athletes by individual and team sports, (2) study the associations between behavioral risk factors associated with obstructive sleep apnea and caffeine use with sleep quality, and (3) characterize the latent sleep profiles of elite youth athletes to optimize the sleep support strategy. Methods: A group (N = 135) of elite national youth athletes completed a self-administered questionnaire consisting of the Pittsburgh Sleep Quality Index (PSQI) and questions pertaining to obstructive sleep apnea, napping behavior, and caffeine use. K-means clustering was used to characterize unique sleep characteristic subgroups based on PSQI components. Results: Athletes reported 7.0 (SD = 1.2) hours of sleep. Out of the total group, 45.2% of the athletes had poor quality sleep (PSQI global >5), with team-sport athletes reporting significantly poorer sleep quality than individual-sport athletes. Multiple logistic regression analysis indicated that sport type significantly correlated with poor sleep quality. The K-means clustering algorithm classified athletes’ underlying sleep characteristics into 4 clusters to efficiently identify athletes with similar underlying sleep issues to enhance interventional strategies.Conclusion: These findings suggest that elite youth team-sport athletes are more susceptible to poorer sleep quality than individual-sport athletes. Clustering methods can help practitioners characterize sleep-related problems and develop efficient athlete support strategies.

Objectives To describe the incidence, prevalence, severity, mechanism and body region of injuries in elite junior Australian football (AF) players over one competitive season in order to help inform injury prevention interventions. Design Prospective cohort, data collected during the 2014 playing season. Methods Player and staff-reported injuries sustained by 562 players from an under-18 state league were entered into an online sports injury surveillance system. An injury was recorded if it led to a missed training session or match. Injury incidence was calculated as the number of injuries per 1000 h of training and competition. Injury severity was defined by the number of days players missed training or competition. Injury mechanism was identified as either contact, non-contact or overuse. Results There were 1192 football-related injuries sustained during the season; the majority (n = 1041, 87.3%) were new, occurred during competition (n = 954, 86%) and led to 4–7 missed days in severity (n = 429, 46%). Injury incidence was 37.2 injuries per 1000 h of exposure. Over half of injuries were contact in mechanism (n = 355, 51%). Most injuries were to the lower limb (n = 720, 60%), with the thigh representing the highest proportion of these. Conclusions This study provides key information as to the aetiology of injury in this level of competition and provides a stronger foundation from which injury prevention studies could be carried out. Future research is well-placed to develop an understanding of the injury risk factors in the elite junior cohort, whilst also reducing injury risk once players transition to the AFL.

Lathlean, TJH, Gastin, PB, Newstead, S, and Finch, CF. Elite junior Australian football players experience significantly different loads across levels of competition and training modes. J Strength Cond Res 32(7): 2031-2038, 2018-Well-developed physical qualities such as high jumping ability, running endurance, acceleration, and speed can help aspiring junior elite Australian football (AF) players transition to the Australian Football League competition. To do so, players need to experience sufficient load to enhance their physical resilience without increasing their risk of negative outcomes in terms of impaired wellness or injury. The aim of this study was to investigate the differences in load for different levels of competition and training modes across one competitive season. Elite junior AF players (n = 562, aged 17.7 ± 0.3, range: 16-18 years) were recruited from 9 teams across the under-18 state league competition in Victoria. All players recorded their training and match intensities according to the session rating of perceived exertion method. Training sessions were categorized according to skills, strength, conditioning, and other activities, whereas matches were identified according to level of competition. The loads in U18 state league matches (656.7 ± 210.9 au) were significantly higher (p = 0.027) than those in school matches (643.3 ± 260.9 au) and those in U18 representative matches (617.2 ± 175.4). Players, who undertook more than one match per week, experienced significantly less load in subsequent matches (p < 0.001). Furthermore, U18 state league training sessions carried the most load when compared with other training modes. This article highlights that different combinations of training and match involvement affect overall player load, which may predispose players to negative outcomes such as impaired wellness or increased injury risk.

Athletes often record details of their training and competitions, supported by information such as environmental conditions, travel, as well as how they felt. However, it is not known how prevalent these practices are in golfers, or how valuable this process is perceived. The purpose of this study was to develop a golf-specific load monitoring tool (GLMT), and establish the content validity and feasibility of this tool amongst high-level golfers. In the first phase of development, 21 experts were surveyed to determine the suitability of items for inclusion in the GLMT. Of the 36 items, 21 received >78% agreement, a requirement to establish content validity and for inclusion in the GLMT. Total duration was the preferred metric for golf-specific activities, whilst rating of perceived exertion (RPE) was preferred for measuring physical training. In the second phase, feasibility of the tool was assessed by surveying 13 high-level male golfers following 28-days of daily GLMT use. All items included in the GLMT were deemed feasible to record, with all players participating in the feasibility study providing high to very high ratings. Golfers responded that they would consider using a load monitoring tool of this nature long term, provided it can be completed in less than five minutes per day.

Despite recent growth and increasing popularity, there is limited knowledge of course choice and career intentions in exercise and sport science. Students in exercise and sport science degrees completed a questionnaire to measure their career intentions and reasons for course choice. Immediate career intentions were most frequently further study, with five year career intentions most often in physiotherapy or sport science areas. The main reasons for course choice were sport association and continuation reasons (love of sport and remaining involved) and interpersonal reasons and means to an end (wanting to work with and help others and to provide career options).

Athlete self-report measures (ASRM) have the potential to provide valuable insight into the training response; however, there is a disconnect between research and practice that needs to be addressed; namely, the measure or methods used in research are not always reflective of practice, or data primarily obtained from practice lacks empirical quality. This commentary reviews existing empirical measures and the psychometric properties required to be considered acceptable for research and practice. This information will allow discerning readers to make a judgment on the quality of ASRM data being reported in research papers. Fastidious practitioners and researchers are also provided with explicit guidelines for selecting and implementing an ASRM and reporting these details in research papers.

© 2017 Human Kinetics, Inc. Monitoring the load placed on athletes in both training and competition has become a very hot topic in sport science. Both scientists and coaches routinely monitor training loads using multidisciplinary approaches, and the pursuit of the best methodologies to capture and interpret data has produced an exponential increase in empirical and applied research. Indeed, the field has developed with such speed in recent years that it has given rise to industries aimed at developing new and novel paradigms to allow us to precisely quantify the internal and external loads placed on athletes and to help protect them from injury and ill health. In February 2016, a conference on "Monitoring Athlete Training Loads-The Hows and the Whys" was convened in Doha, Qatar, which brought together experts from around the world to share their applied research and contemporary practices in this rapidly growing field and also to investigate where it may branch to in the future. This consensus statement brings together the key findings and recommendations from this conference in a shared conceptual framework for use by coaches, sport-science and-medicine staff, and other related professionals who have an interest in monitoring athlete training loads and serves to provide an outline on what athlete-load monitoring is and how it is being applied in research and practice, why load monitoring is important and what the underlying rationale and prospective goals of monitoring are, and where athlete-load monitoring is heading in the future.

ObjectivesTo determine whether specific injury measures were associated with team performance in the Australian Football League (AFL).Methods15 289 injuries caused players from 18 teams to miss 51 331 matches between 1997 and 2016. Data were aggregated to the team level. We analysed the associations among injury measures and team performance (reaching finals/playoffs and specific ladder/table position). Injury measures per team included: injury incidence, injury severity, injury burden, player match availability and percentage of the full player roster injured. We also weighted injury measures by five measures of player value.ResultsAFL teams’ injury burden and player match availability were associated with final table position (r2=0.03, pDiscussion and potential implicationsThe impact of injury (burden weighted by best and fairest) explained up to 12% of the variation in final table position—this is particularly relevant to making/not making playoffs as well as home ground/travel advantages for those teams that make the one-game format of AFL playoffs (not home-away or best of seven format).

Jessiman, SW, Harvey, B, Corrigan, SL, and Gastin, PB. Training and competition activity profiles of Australian football field umpires. J Strength Cond Res 34(10): 2956-2964, 2020-The purpose of this study was to determine the activity profiles of Australian football (AF) field umpires during training and competition, and subsequently assess the specificity to competition of locomotor training sessions. Microtechnology incorporating a 5-Hz (interpolated to 15 Hz) global positioning system sensor tracked the movements of 24 field umpires during matches at the Melbourne Cricket Ground and fitness and skill-based training sessions before competition. Paired t-tests or Wilcoxon signed-rank tests determined whether significant differences existed between single training session and competition paired samples, with Cohen's d effect size and percent differences describing the magnitude of the training-competition differences. Absolute measures of total (d = 5.4; percent difference = 85.8%) and high-speed distance (14.4 km·h) (1.0; 36.9%), as well as accelerations (3.3; 106.3%) and decelerations (3.2; 107.5%) were significantly greater during competition compared with training (p0.001). When standardized for time, high-speed distance (1.4; 52.0%), sprint distance (23.0 km·h) (0.9; 121.5%), sprint efforts (1.0; 107.4%), and high acceleration (1.1; 114.3%) and deceleration (0.6; 66.7%) events (≥3 m·s) were greater during training (p0.001). No difference between training and competition was observed for relative distance. A single training session did not match the volume of work during competition, due primarily to large differences in duration. By contrast, training sessions were higher in intensity, likely to compensate for the shorter duration of training. Further research is required to determine whether the total weekly training load is sufficient to maintain and develop the competition-specific fitness of AF field umpires.

Gastin, PB, Hunkin, SL, Fahrner, B, and Robertson, S. Deceleration, acceleration, and impacts are strong contributors to muscle damage in professional Australian football. J Strength Cond Res 33(12): 3374-3383, 2019-The purpose of this study was to investigate the relationships between serum creatine kinase [CK], an indirect marker of muscle damage, and specific indices of match load in elite Australian football. Twenty-six professional players were assessed during a competitive Australian Football League (AFL) season. [CK] was collected 24-36 hours before match and 34-40 hours after match during 8 in-season rounds. An athlete-tracking technology was used to quantify match load. Generalized estimating equations and random forest models were constructed to determine the extent to which match-load indices and pre-match [CK] explained post-match [CK]. There was a 129 ± 152% increase in [CK] in response to AFL competition. Generalized estimating equations found that number of impacts3g (p = 0.004) and game time (p = 0.016) were most strongly associated with post-match [CK]. Random forest, with considerably lower errors (130 vs. 316 U·L), found deceleration, acceleration, impacts3g, and sprint distance to be the strongest predictors. Pre-match [CK] accounted for 11% of post-match [CK], and considerable interindividual and intraindividual variability existed in the data. Creatine kinase, an indicator of muscle damage, was considerably elevated as a result of AFL competition. Parametric and machine-learning analysis techniques found several indices of physical load associated with muscle damage during competition, with impacts3g and high-intensity running variables as the strongest predictors. [CK] may be used as a global measure of muscle damage in field team sports such as AF, yet with some caution given cost, invasiveness, and inherent variability. Quantifying physical load and the responses to that load can guide athlete management decision-making and is best undertaken within a suite of practical, sport-specific measures, where data are interpreted individually and with an understanding of the limitations.

The purpose of this study was to assess the validity of a global positioning system (GPS) tracking system to estimate energy expenditure (EE) during exercise and field-sport locomotor movements. Twenty-seven participants each completed a 90-min exercise session on an outdoor synthetic futsal pitch. During the exercise session, they wore a 5-Hz GPS unit interpolated to 15 Hz and a portable gas analyzer that acted as the criterion measure of EE. The exercise session was composed of alternating 5-minute exercise bouts of randomized walking, jogging, running, or a field-sport circuit (×3) followed by 10 min of recovery. One-way analysis of variance showed significant (P < .01) and very large underestimations between GPS metabolic power– derived EE and oxygen-consumption (VO2) -derived EE for all field-sport circuits (% difference ≈ –44%). No differences in EE were observed for the jog (7.8%) and run (4.8%), whereas very large overestimations were found for the walk (43.0%). The GPS metabolic power EE over the entire 90-min session was significantly lower (P < .01) than the VO2 EE, resulting in a moderate underestimation overall (–19%). The results of this study suggest that a GPS tracking system using the metabolic power model of EE does not accurately estimate EE in field-sport movements or over an exercise session consisting of mixed locomotor activities interspersed with recovery periods; however, is it able to provide a reasonably accurate estimation of EE during continuous jogging and running.

This study quantified the training load experienced by elite aerial skiers. Nine elite female aerial skiers were monitored during 16 training sessions over a 13 day period. Time-motion, landing impact and heart rate (HR) data were measured from 688 jumps using integrated GPS, accelerometer and HR transmitters while rating of perceived exertion (RPE) was taken using Borg’s scale. Each jump was delineated into five components from the GPS time-motion data to determine the work to rest ratios. Participants completed 16 ± 3 jumps per session with a work to rest ratio of 1.9:1 Heart rates averaged 65 ± 3.1% HRmax and peaked at 85 ± 4.4% HRmax while and an RPE score of 12 ± 1 was evoked. Landing impacts were significantly higher (p ≤ 0.001) when participants jumped off ramps with a larger take-off angle or when they completed jumps with a mid-air rotation. The training load experienced by elite aerial skiers may be causative of the high incidence of injuries reported. Significantly differing levels of impact lo...

The purpose of the present study was to determine the efficacy of the multi-component training distress scale (MTDS), in monitoring swimmers prior to national competition. Twenty-one national-level adolescent swimmers completed eight weeks of testing. Once a week participants completed an 8 × 50 m sprint test, vertical jump test, sit-and-reach test, the MTDS and the Recovery-Stress Questionnaire for Athletes (REST-Q). All testing was incorporated into the swimmers' normal training programme. The REST-Q accounted for the following variances in performance: flexibility (14.6%, p = 0.009), power output (17.7%, p = 0.003), swimming speed (15.5%, p = 0.006) and swimming endurance (17.5%, p = 0.002). In comparison, the MTDS accounted for the following variances in performance: flexibility (12.1%, p = 0.095), power output (16.4%, p = 0.023), swimming speed (20.5%, p = 0.003) and swimming speed endurance (23.8%, p = 0.001). The findings of the current study suggest that both the REST-Q Sport and the MTDS have the capacity to predict performance on a range of fitness components associated with swimming.