Classification of Force-Time Metrics Into Lower-Body Strength Domains.

ABSTRACT: Geneau, MC, Carey, DL, Gastin, PB, Robertson, S, and James, LP. Classification of force-time metrics into lower-body strength domains. J Strength Cond Res 38(9): 1561-1567, 2024-The purpose of this study was to classify force-time metrics into distinct lower-body strength domains using a systematic data reduction analysis. A cross-sectional design was used, whereby competitive field sport athletes ( F = 39, M = 96) completed a series of drop jumps, squat jumps, countermovement jumps (CMJs), loaded CMJs, and 2 isometric tasks on portable force platforms, resulting in a total of 285 force-time performance metrics. The metrics were split into 4 test "families" and each was entered into a sparse principal component analysis (sPCA) model. A single metric from each component of each family-specific sPCA were selected based on the loading, reliability, and simplicity of the metric and entered into a second sPCA that included metrics across all tests. The final sPCA revealed 7 principal components each containing 2 metrics and explained a total of 53% variance of the dataset. The final principal components were interpreted as 7 lower-body strength domains: (a) dynamic force, (b) dynamic timing, (c) early isometric, (d) maximal isometric, (e) countermovement velocity, (f) reactive output, and (g) reactive timing. The findings demonstrate that a total of 7 metrics from a drop jump, CMJ, and isometric test can be used to represent ∼50% of variance in lower-body strength performance of field sport athletes. These results can help guide and simplify the lower-body strength diagnosis process in field sport athletes.

Relationships between contract status and player performance in the Australian Football League.

This study analysed the extent to which player performance differs within the Australian Football League (AFL) with respect to the status of a player's contract. AFL Player Ratings (AFLPR) and contract data were obtained during the 2013-2020 AFL seasons for all 827 players listed by an AFL club at the beginning of the 2020 season. A model of "expected performance" was created allowing for an exploration into the differential with actual performance as a function of contract status. Paired t-tests indicated that there was a difference in performance pre- and post-signing their contract for players who signed mid-season (mean change and 95% confidence interval of -1.48 ± 0.93 and -0.49 ± 0.48 AFLPR, at ten match intervals for those in- and out-of-contract at the conclusion of that year's season, respectively). Further differences existed between the groups of players who signed mid-season, as compared to those who signed during the off-season. Correlation analyses indicated that more consistent performers are somewhat less likely to see a reduction in performance post signing as compared to less consistent performers. The applications of these findings have the potential to support organisational decisions relating to the timing and nature of player contracting.

The Youth Patient and Public Involvement Café-A youth-led model for meaningful involvement with children and young people.

INTRODUCTION: There are few meaningful frameworks or toolkits that exist for involvement with young people. Coproduction is a more recent patient and public involvement (PPI) approach that emphasizes the importance of power-sharing, to set young people as equal partners in the research process. This paper explores the successes and challenges encountered by one coproduced PPI space for young people. METHODS: This paper is written by a team of young people who developed and worked on the Youth PPI Café over a period of 18 months. It explores how we developed a youth-led space for involvement in research. The authors have reflected on their experiences, providing examples of how youth PPI and coproduction were delivered in the NHS, in practice. RESULTS: By working 'with' young people, rather than 'for' them, we offer insights into the successes and challenges of an entirely youth-led involvement space. Despite being effective in shaping mental health research for children and young people, we faced challenges with tokenism, resourcing and diversity and inclusion. CONCLUSIONS: Involving youth meaningfully in research has the potential to inform studies at a macro- and microlevel, enabling positive change within research and within the systems that support young people. PATIENT OR PUBLIC CONTRIBUTION: Young people aged 16-24 years with lived experience were included at every stage of this project, from formulation to the delivery and development of the group, to the preparation of this manuscript and its dissemination. Sussex Partnership NHS Foundation Trust's charity 'Heads On' provided funding for this study.

The impact of Patient and Public Involvement in the SlowMo study: Reflections on peer innovation.

BACKGROUND: The SlowMo study demonstrated the effects of SlowMo, an eight-session digitally supported reasoning intervention, on paranoia in a large-scale randomized-controlled trial with 362 participants with schizophrenia-spectrum psychosis. AIM: The current evaluation aimed to investigate the impact of Patient and Public Involvement (PPI) in the SlowMo study. METHOD: PPI members were six women and three men from Sussex, Oxford and London with experience of using mental health services for psychosis. They received training and met at least 3-monthly throughout the project. The impact of PPI was captured quantitatively and qualitatively through (i) a PPI log of recommendations and implementation; (ii) written subjective experiences of PPI members; (iii) meeting minutes; and (iv) outputs produced. RESULTS: The PPI log revealed 107 recommendations arising from PPI meetings, of which 87 (81%) were implemented. Implementation was greater for recruitment-, data collection- and organization-related actions than for dissemination and emergent innovations. Qualitative feedback revealed impacts on study recruitment, data collection, PPI participants' confidence, knowledge, career aspirations and society more widely. Outputs produced included a film about psychosis that aired on BBC primetime television, novel webpages and journal articles. Barriers to PPI impact included geography, travel, funding, co-ordination and well-being. DISCUSSION: A future challenge for PPI impact will be the extent to which peer innovation (innovative PPI-led ideas) can be supported within research study delivery. PATIENT AND PUBLIC CONTRIBUTION: Planned Patient and Public Contribution in SlowMo comprised consultation and collaboration in (i) design, (ii) recruitment, (iii) qualitative interviews and analysis of service users' experiences of SlowMo therapy and (iv) dissemination.

Gender-specific psychosocial stressors influencing mental health among women elite and semielite athletes: a narrative review.

Elite and semielite athletes commonly experience mental health concerns and disorders. Compared with men athletes, women athletes are at greater risk of a range of psychological stressors that contribute to health concerns and mental health disorders, which can impact their career satisfaction and longevity. In order to address and improve the mental health of women athletes, it is necessary to simultaneously tackle the gender specific psychosocial stressors that contribute to mental health outcomes. This narrative review examines the gender-specific stressors that affect mental health and well-being in women athletes, some of which are modifiable. Psychosocial stressors identified include exposure to violence, be it psychological, physical or sexual in nature, which can result in a myriad of acute and long-lasting symptoms; and inequities as reflected in pay disparities, under-representation in the media, fewer opportunities in leadership positions and implications associated with family planning and motherhood. Strategies to promote mental health in women athletes should be considered, and where possible, should proactively address gender-specific stressors likely to influence mental health in order to maximise positive outcomes in women athletes.

The influence of tactical and match context on player movement in football.

Player movement metrics in football such as speed and distance are typically analysed as aggregates, sometimes outside of any specific tactical or match context. This research adds context to a player's movement over the course of a match by analysing movement profiles s and bringing together tools from the sport science and sports analytics literature. Position-specific distributions of player movement metrics: speed, acceleration and tortuosity were compared across phases of play and in-game win probability using 25 Hz optical player tracking data from all 52 matches at the 2019 FIFA Women's World Cup. Comparing the distributions using the Kolmogorov-Smirnov test and Wasserstein distances, differences were identified in these movement profiles across, in and out of possession phases, with small negligible overall positional trends across in-game win probabilities. In-game win probabilities are used in tandem with phases to present a player specific case study. The results demonstrate how sports analytics metrics can be used to contextualise a subset of movement metrics from sport science and provide a framework for analysis of further movement metrics and sports analytics modelling approaches.

The influence of individual, task and environmental constraint interaction on skilled behaviour in Australian Football training.

An important consideration for sport practitioners is the design of training environments that facilitate skill learning. This study presented a method to determine individual (age, games played, height, mass, and position), environmental (activity type) and task (pressure and possession time) constraint interaction to evaluate player training behaviour. Skill actions (n = 7301) were recorded during training activities (n = 209) at a single professional Australian Football club and four measures of player behaviour were determined: disposal frequency, kick percentage, pressure, and possession time. K-means clustering assigned training activities into four groups, with regression trees used to determine the interaction between constraints and their influence on disposal frequency and type. For most regression trees, only the environmental constraint was included. This showed all players adapted similarly to the constraints of each training activity. In one exception, a critical value of 60 games experience was identified as an individual constraint which interacted with activity type one to influence disposal frequency. Practically, this individual constraint value could be used to guide training design by grouping players of similar experience together. This study is presented as a practical tool for sport practitioners, which considers constraint interaction, to evaluate player behaviour and inform training design.

Classification of Players Across the Australian Rules Football Participation Pathway Based on Physical Characteristics.

ABSTRACT: Haycraft, JAZ, Kovalchik, S, Pyne, DB, and Robertson, S. Classification of players across the Australian Rules football participation pathway based on physical characteristics. J Strength Cond Res 36(3): 702-709, 2022-This study investigated the utility of physical fitness and movement ability tests to differentiate and classify players into Australian Football League (AFL) participation pathway levels. Players (n = 293, age 10.9-19.1 years) completed the following tests; 5-m, 10-m, and 20-m sprint, AFL planned agility, vertical jump (VJ), running VJ, 20-m multistage fitness test (MSFT), and athletic ability assessment. A multivariate analysis of variance between AFL participation pathway levels was conducted, and a classification tree determined the extent to which players could be allocated to relevant levels. The magnitude of differences between physical fitness and movement ability were level-dependent, with the largest standardized effect size (ES) between Local U12, Local U14, and older levels for most physical fitness tests (ES: -4.64 to 5.02), except the 5-m and 10-m sprint. The 20-m, 5-m, AFL agility, 20-m MSFT, overhead squat, and running VJ (right) contributed to the classification model, with 57% overall accuracy reported (43% under cross-validation). National U16 players were easiest to classify (87%), while National U18 players were most difficult (0%). Physical fitness tests do not seem to differentiate between players after selection into AFL talent pathway levels. Other attributes (i.e., skill, psychological, and sociocultural) should be prioritized over physical fitness and movement attributes by selectors/coaches when considering selection of talented players.

Apples and oranges? Comparing player performances between the Australian Football League and second-tier leagues.

This study developed a model to determine the extent to which player performance objectively differs between various Australian football (AF) leagues. Champion Data (CD) ranking points were obtained during the 2016-2019 seasons, for all players across the Australian Football League (AFL) and the 10 main second-tier AF leagues. Data pertaining to each player's age, playing position and the AF leagues in which they competed in were also collected. Phase One investigated the difference between the AFL and the senior second-tier leagues in which AFL affiliate teams participate. Post-hoc tests indicated that objective player performance was substantially different between the AFL and each of the four senior second-tier leagues (effects ranging from 16.8 to 21.6 CD ranking points). Phase Two investigated the difference between the second-tier leagues from which players are traditionally drafted by an AFL club. Post-hoc tests indicated that objective player performance was substantially different between the four senior second-tier leagues as well as the under-18 national championships, in comparison to each of the reserve and under-18 state leagues. Professional sporting organisations may utilise the methods provided here as an example of what could be implemented to support decisions regarding player contracting, recruitment and team selection.

Classification of Australian football kick types in-situation via ankle-mounted inertial measurement units.

The utility of inertial measurement units (IMUs) for sporting skill and performance analysis during training and competition is advantageous for enhancing the objectivity of athlete monitoring. This study aimed to classify Australian Rules football (AF) kick types in an applied environment using ankle-mounted IMUs. IMUs and video capture of a controlled protocol, including four kick types at varying distances, were recorded during a single testing session with female AF athletes (n = 20). Processed IMU data were modelled using support vector machine classifier, random forest, and k-nearest neighbour algorithms under a 2-Kick, 4-Kick, and kick distance (10, 20, 30 m) conditions. The random forest model showed the highest results for overall classification accuracy (83% 2-Kick and 80% 4-Kick), test F1-score (0.76 2-Kick and 0.81 4-Kick), and AUC score (0.58 2-Kick and 0.60 4-Kick). Kick distance classification showed a model test and class weighted F1-score of 0.63 and overall accuracy of 64%, respectively. This study highlights the potential for an applied semi-automated AF training kick detection and type classification system using IMUs.

Long-term influence of technical, physical performance indicators and situational variables on match outcome in male professional Chinese soccer.

This study aimed to determine whether the role of technical, physical performance indicators and situational variables in determining match outcome has varied from a long-term analysis (seasons 2012 to 2017) of the Chinese Soccer Super League (CSL). The sample included 1,429 matches where 17 technical performance-related indicators, 11 physical performance-related indicators and two situational variables (match location and quality of opposition) were analysed. Three binary logistic regression models (inclusion of different variables) were used to measure the level of association between factors and match outcome over the six seasons studied. Results of models 1 and 2 revealed that shots on target, possession, total distance in ball possession, total distance out of ball possession, and match location exerted a decreased influence on winning the matches from 2012 to 2014 seasons. However, these indicators play a more important role in winning matches from 2014 to 2017 seasons. Additionally, the quality of opposition has a continuously increased negative effect on the match outcome. In model 3, more variables, such as high-speed distance, high-speed out of ball possession, had a meaningful influence on winning the match. These results provide valuable information about performance indicators and situational variables on winning the matches from a long-term approach.

Application of a continuous pressure metric for Australian football.

Pressure is an important constraint on sports performance and is typically measured through manual notational analysis. A continuous representation of pressure, along with semi-automated measurement, would serve to improve the efficiency of practice design and analysis, as well as provide additional context to player competition performance. Using spatiotemporal data collected from wearable tracking devices, the present study applied Kernel Density Estimation to estimate the density of players, relative to the ball carrier, at point of skill execution during elite Australian Football training. Two environmental constraints were measured (area per player and number of players) to determine the relationship between these training design manipulations and density. Density was also compared with existing notational analysis measurements of pressure. Results indicated that a higher density on skills was associated with successful skill executions. The opposite relationship was found between notational analysis pressure measurement and skill effectiveness. A strong inverse relationship was found between environmental constraint manipulation and density, whereby increasing field size and playing number decreased the density on skill involvements. The findings offer insight into the continuous measurement of pressure and encourage practitioners to utilize training design manipulations to influence density as a constraint on skills.

A change point approach to analysing the match activity profiles of team-sport athletes.

In team-sport, physical and skilled output is often described via aggregate parameters including total distance and number of skilled involvements. However, the degree to which these output change throughout a team-sport match, as a function of time, is relatively unknown. This study aimed to identify and describe segments of physical and skilled output in team-sport matches with an example in Australian Football. The relationship between the number of change points and level of similarity was also quantified. A binary segmentation algorithm was applied to the velocity time series, collected via wearable sensors, of 37 Australian football players (age: 23 ± 4 years, height: 187 ± 8 cm, mass: 86 ± 9 kg). A change point quotient of between 1 and 15 was used. For these quotients, descriptive statistics, spectral features and a sum of skilled involvements were extracted. Segment similarity for each quotient was evaluated using a random forest model. The strongest classification features in the model were spectral entropy and skewness. Offensive and defensive involvements were the weakest features for classification, suggesting skilled output is dependent on match circumstances. The methodology presented may have application in comparing the specificity of training to matches and designing match rotation strategies.

Machine and deep learning for sport-specific movement recognition: a systematic review of model development and performance.

Objective assessment of an athlete's performance is of importance in elite sports to facilitate detailed analysis. The implementation of automated detection and recognition of sport-specific movements overcomes the limitations associated with manual performance analysis methods. The object of this study was to systematically review the literature on machine and deep learning for sport-specific movement recognition using inertial measurement unit (IMU) and, or computer vision data inputs. A search of multiple databases was undertaken. Included studies must have investigated a sport-specific movement and analysed via machine or deep learning methods for model development. A total of 52 studies met the inclusion and exclusion criteria. Data pre-processing, processing, model development and evaluation methods varied across the studies. Model development for movement recognition were predominantly undertaken using supervised classification approaches. A kernel form of the Support Vector Machine algorithm was used in 53% of IMU and 50% of vision-based studies. Twelve studies used a deep learning method as a form of Convolutional Neural Network algorithm and one study also adopted a Long Short Term Memory architecture in their model. The adaptation of experimental set-up, data pre-processing, and model development methods are best considered in relation to the characteristics of the targeted sports movement(s).

Collective team behaviour of Australian Rules football during phases of match play.

Using the spatiotemporal characteristics of players, the primary aim of this study was to determine whether differences in collective team behaviour exist in Australian Rules football during different phases of match play. The secondary aim was to determine the extent to which collective team behaviour differed between competing teams and match half. Data was collected via 10 Hz global positioning system devices from a professional club during a 2 × 20 min, 15-v-15-match simulation drill. Five spatiotemporal variables from each team (x centroid, y centroid, length, width, and surface area) were collected and analysed during offensive, defensive, and contested phases. A multivariate analysis of variance comparing phase of match play (offensive, defensive, contested), Team (A & B), and Half (1 & 2) revealed that x-axis centroid and y-axis centroid showed considerable variation during all phases of match play. Length, width, and surface area were typically greater during the offensive phase comparative to defensive and contested phases. Clear differences were observed between teams with large differences recorded for length, width, and surface area during all phases of match play. Spatiotemporal variables that describe collective team behaviour may be used to understand team tactics and styles of play.

A rule induction framework for the determination of representative learning design in skilled performance.

Representative learning design provides a framework for the extent to which practice simulates key elements of a performance setting. Improving both the measurement and analysis of representative learning design would allow for the refinement of sports training environments that seek to replicate competition conditions and provide additional context to the evaluation of athlete performance. Using rule induction, this study aimed to develop working models for the determination of high frequency, representative events in Australian Rules football kicking. A sample of 9005 kicks from the 2015 Australian Football League season were categorised and analysed according to the following constraints: type of pressure, kick distance, possession source, time in possession, velocity and kick target. The Apriori algorithm was used to develop two models. The first consisted of 10 rules containing the most commonly occurring constraint sets occurring during the kick in AF, with support values ranging from 0.15 to 0.22. None of the rules contained more than three constraints and confidence values ranged from 0.63 to 0.84. The second model considered ineffective and effective kick outcomes and displayed 70% classification accuracy. This research provides a measurement approach to determine the degree of representativeness of sports practice and is directly applicable to various team sports.

The influence of match phase and field position on collective team behaviour in Australian Rules football.

This study investigated the influence of match phase and field position on collective team behaviour in Australian Rules football (AF). Data from professional male athletes (years 24.4 ± 3.7; cm 185.9 ± 7.1; kg 85.4 ± 7.1), were collected via 10 Hz global positioning system (GPS) during a competitive AFL match. Five spatiotemporal metrics (x-axis centroid, y-axis centroid, length, width, and surface area), occupancy maps, and Shannon Entropy (ShannEn) were analysed by match phase (offensive, defensive, and contested) and field position (defensive 50, defensive midfield, forward midfield, and forward 50). A multivariate analysis of variance (MANOVA) revealed that field position had a greater influence on the x-axis centroid comparative to match phase. Conversely, match phase had a greater influence on length, width, and surface area comparative to field position. Occupancy maps revealed that players repositioned behind centre when the ball was in their defensive half and moved forward of centre when the ball was in their forward half. Shannon Entropy revealed that player movement was more variable during offence and defence (ShannEn = 0.82-0.93) compared to contest (ShannEn = 0.68-0.79). Spatiotemporal metrics, occupancy maps, and Shannon Entropy may assist in understanding the game style of AF teams.

Deceleration, Acceleration, and Impacts Are Strong Contributors to Muscle Damage in Professional Australian Football.

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 impacts >3g (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, impacts >3g, 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 impacts >3g 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.

Please Don't Move-Evaluating Motion Artifact From Peripheral Quantitative Computed Tomography Scans Using Textural Features.

Most imaging methods, including peripheral quantitative computed tomography (pQCT), are susceptible to motion artifacts particularly in fidgety pediatric populations. Methods currently used to address motion artifact include manual screening (visual inspection) and objective assessments of the scans. However, previously reported objective methods either cannot be applied on the reconstructed image or have not been tested for distal bone sites. Therefore, the purpose of the present study was to develop and validate motion artifact classifiers to quantify motion artifact in pQCT scans. Whether textural features could provide adequate motion artifact classification performance in 2 adolescent datasets with pQCT scans from tibial and radial diaphyses and epiphyses was tested. The first dataset was split into training (66% of sample) and validation (33% of sample) datasets. Visual classification was used as the ground truth. Moderate to substantial classification performance (J48 classifier, kappa coefficients from 0.57 to 0.80) was observed in the validation dataset with the novel texture-based classifier. In applying the same classifier to the second cross-sectional dataset, a slight-to-fair (κ = 0.01-0.39) classification performance was observed. Overall, this novel textural analysis-based classifier provided a moderate-to-substantial classification of motion artifact when the classifier was specifically trained for the measurement device and population. Classification based on textural features may be used to prescreen obviously acceptable and unacceptable scans, with a subsequent human-operated visual classification of any remaining scans.

Evaluating strategic periodisation in team sport.

The planned peaking for matches or events of perceived greatest priority or difficulty throughout a competitive season is commonplace in high-level team sports. Despite this prevalence in the field, little research exists on the practice. This study aimed to provide a framework for strategic periodisation which team sport organisations can use to evaluate the efficacy of such plans. Data relating to factors potentially influencing the difficulty of matches were obtained for games played in the 2014 Australian Football League season. These included the match location, opposition rank, between-match break and team "form". Binary logistic regression models were developed to determine the level of association between these factors and match outcome (win/loss). Models were constructed using "fixed" factors available to clubs prior to commencement of the season, and then also "dynamic" factors obtained at monthly intervals throughout the in-season period. The influence of playing away from home on match difficulty became stronger as the season progressed, whilst the opposition rank from the preceding season was the strongest indicator of difficulty across all models. The approaches demonstrated in this paper can be used practically to evaluate both the long- and short-term efficacy of strategic periodisation plans in team sports as well as inform and influence coach programming.