Weak Relationships between Stint Duration, Physical and Skilled Match Performance in Australian Football

Contextual factors associated with running demands in elite Australian football: a scoping review

OBJECTIVES

To identify and summarise the contextual factors associated with running demands in elite male Australian football (AF) gameplay that have been reported in the literature.

DESIGN

Scoping review.

METHODS

A contextual factor in sporting gameplay is a variable associated with the interpretation of results, yet is not the primary objective of gameplay. Systematic literature searches were performed in four databases to identify what contextual factors associated with running demands in elite male AF have been reported: Scopus, SPORTDiscus, Ovid Medline and CINAHL, for terms constructed around Australian football AND running demands AND contextual factors. The present scoping review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), and narrative synthesis was conducted.

RESULTS AND CONCLUSION

A total of 36 unique articles were identified by the systematic literature search, which included 20 unique contextual factors. The most studied contextual factors were position (n = 13), time in game (n = 9), phases of play (n = 8), rotations (n = 7) and player rank (n = 6). Multiple contextual factors, such as playing position, aerobic fitness, rotations, time within a game, stoppages, and season phase appear to correlate with running demands in elite male AF. Many identified contextual factors have very limited published evidence and thus additional studies would help draw stronger conclusions.

A method to inform team sport training activity duration with change point analysis

Duration is a key component in the design of training activities in sport which aim to enhance athlete skills and physical qualities. Training duration is often a balance between reaching skill development and physiological targets set by practitioners. This study aimed to exemplify change point time-series analyses to inform training activity duration in Australian Football. Five features of player behaviour were included in the analyses: disposal frequency, efficiency, pressure, possession time and player movement velocity. Results of the analyses identified moments of change which may be used to inform minimum or maximum activity durations, depending on a practitioner’s objectives. In the first approach, a univariate analysis determined change points specific to each feature, allowing practitioners to evaluate activities according to a single metric. In contrast, a multivariate analysis considered interactions between features and identified a single change point, reflecting the moment of overall change during activities. Six iterations of a training activity were also evaluated resulting in common change point locations, between 196 and 252 seconds, which indicated alterations to player behaviour between this time period in the training activities conduction. Comparisons of feature segments before and after change points revealed the extent to which player behaviour changed and can guide such duration decisions. These methods can be used to evaluate athlete behaviour and inform training activity durations.

Tracking Systems in Team Sports: A Narrative Review of Applications of the Data and Sport Specific Analysis

Seeking to obtain a competitive advantage and manage the risk of injury, team sport organisations are investing in tracking systems that can quantify training and competition characteristics. It is expected that such information can support objective decision-making for the prescription and manipulation of training load. This narrative review aims to summarise, and critically evaluate, different tracking systems and their use within team sports. The selection of systems should be dependent upon the context of the sport and needs careful consideration by practitioners. The selection of metrics requires a critical process to be able to describe, plan, monitor and evaluate training and competition characteristics of each sport. An emerging consideration for tracking systems data is the selection of suitable time analysis, such as temporal durations, peak demands or time series segmentation, whose best use depends on the temporal characteristics of the sport. Finally, examples of characteristics and the application of tracking data across seven popular team sports are presented. Practitioners working in specific team sports are advised to follow a critical thinking process, with a healthy dose of scepticism and awareness of appropriate theoretical frameworks, where possible, when creating new or selecting an existing metric to profile team sport athletes.

Modelling the Influence of Task Constraints on Goal Kicking Performance in Australian Rules Football

Background

The primary aim of this study was to determine the influence of task constraints, from an ecological perspective, on goal kicking performance in Australian football. The secondary aim was to compare the applicability of three analysis techniques; logistic regression, a rule induction approach and conditional inference trees to achieve the primary aim. In this study, an ecological perspective has been applied to explore the impact of task constraints on shots on goal in the Australian Football League, such as shot type, field location and pressure. Analytical techniques can increase the understanding of competition environments and the influence of constraints on skilled events. Differing analytical techniques can produce varying outputs styles which can impact the applicability of the technique. Logistic regression, Classification Based on Associations rules and conditional inference trees were conducted to determine constraint interaction and their influence on goal kicking, with both the accuracy and applicability of each approach assessed.

Results

Each analysis technique had similar accuracy, ranging between 63.5% and 65.4%. For general play shots, the type of pressure and location particularly affected the likelihood of a shot being successful. Location was also a major influence on goal kicking performance from set shots.

Conclusions

When different analytical methods display similar performance on a given problem, those should be prioritised which show the highest interpretability and an ability to guide decision-making in a manner similar to what is currently observed in the organisation.

Peak Movement and Technical Demands of Professional Australian Football Competition

ABSTRACT

Johnston, RD, Murray, NB, Austin, DJ, and Duthie, G. Peak movement and technical demands of professional Australian football competition. J Strength Cond Res 35(10): 2818-2823, 2021-The aim of this study was to determine the average peak movement and technical demands of professional Australian football (AF) across a number of period durations using an observational cohort design. This information will be able to guide duration-specific intensities for training drills. Microtechnology and technical performance data were recorded across 22 games of the 2017 AF League season. The peak 1-, 3-, 5-, 7-, and 10-minute rolling periods were determined from each game for each player for each frequency of skill involvements. Average speed (m·min-1) and accelerometer load (PlayerLoad; PL·min-1) were used as measures of physical output, and any disposal of the football or tackle was used as a technical involvement. Linear mixed models and Cohen's effect size (ES) statistic were used to determine the impact technical involvements had on movement profiles. There were substantial reductions in average speed across each duration as the number of technical involvements increased, other than for the 10-minute period. The reductions in speed were greatest during the 1-minute period for 1 (ES = -0.59 ± 0.13), 2 (ES = -1.96 ± 0.17), and 3 (ES = -2.39 ± 0.27) involvements. Similarly, less pronounced reductions were seen for accelerometer load, other than during the 7- and 10-minute periods where there were small to moderate increases in load for periods with technical involvements. Players may have to perform as many as 3 technical involvements a minute while covering 150-160 m·min-1. This information provides coaches with the peak speed, accelerometer load, and technical demands of competition. There are reductions in movement profiles as the number of technical involvements increases.

Evolution of Physical Demands of Australian Football League Matches from 2005 to 2017: A Systematic Review and Meta-Regression

BACKGROUND

There is extensive research investigating the match demands of players in the Australian Football League (AFL).

OBJECTIVE

This systematic literature review and meta-regression sought to analyse the evolution of in-game demands in AFL matches from 2005 to 2017, focusing on the relationship between volume and intensity.

METHODS

A systematic search of Ovid MEDLINE, Embase, Emcare, Scopus, SPORTDiscus, and Cochrane Library databases was conducted. Included studies examined the physical demands of AFL matches utilising global positioning system (GPS) technology. Meta-regression analysed the shift in reported volume (total distance and total match time) and intensity (metres per minute [m.min^-1], sprint duration and acceleration) metrics for overall changes, across quarters and positional groups (forwards, nomadics and defenders) from 2005 to 2017 inclusive and for each year between 2005 and 2007, 2007 and 2010, 2010 and 2012, and 2012 and 2015/2017 breakpoints.

RESULTS

Distance (p = 0.094), m.min^-1 (p = 0.494), match time (p = 0.591), time over 18 km·h^-1 (p = 0.271), and number of accelerations greater than 4 km·h^-1 (p = 0.498) and 10 km·h^-1 (p = 0.335) in 1 s did not change from 2005 to 2017. From 2005 to 2007 volume decreased (- 6.10 min of match time; p = 0.010) and intensity increased (6.8 m.min^-1 increase; p = 0.023). Volume and intensity increased from 2007 to 2010, evidenced by increases in total distance (302 m; p = 0.039), time over 18 km·h^-1 (0.31 min; p = 0.005), and number of accelerations greater than 4 km·h^-1 (41.1; p = 0.004) and 10 km·h^-1 (3.6; p = 0.005) in 1 s. From 2010 to 2012, intensity decreased, evidenced by reductions in metres per minute (- 4.3; p = 0.022), time over 18 km·h^-1 (- 0.93 min; p < 0.001), and number of accelerations greater than 4 km·h^-1 (- 104.4; p < 0.001) and 10 km·h^-1 (- 8.3; p < 0.001) in 1 s, whilst volume stabilised with no changes in distance (p = 0.068) and match time (p = 0.443). From 2012 to 2015/2017 volume remained stable and intensity increased with time over 18 km·h^-1 (0.27 min; p = 0.008) and number of accelerations greater than 4 km·h^-1 (31.6; p = 0.016) in 1 s increasing.

CONCLUSIONS

Changes in volume and intensity of AFL match demands are defined by discrete periods from 2007 to 2010 and 2010 to 2012. The interaction of rule and interpretation changes and coaching strategies play a major role in these evolutionary changes. In turn, modified game styles impact player game demands, training, and selection priorities. Standardisation and uniformity of GPS data reporting is recommended due to inconsistencies in the literature.

Methodological Considerations for Furthering the Understanding of Constraints in Applied Sports

Commonly classified as individual, task or environmental, constraints are boundaries which shape the emergence of functional movement solutions. In applied sport, an ongoing challenge is to improve the measurement, analysis and understanding of constraints to key stakeholders. Methodological considerations for furthering these pursuits should be centred around an interdisciplinary approach. This integration of methodology and knowledge from different disciplines also encourages the sharing of encompassing principles, concepts, methods and data to generate new solutions to existing problems. This narrative review discusses how a number of rapidly developing fields are positioned to help guide, support and progress an understanding of sport through constraints. It specifically focuses on examples from the fields of technology, analytics and perceptual science. It discusses how technology is generating large quantities of data which can improve our understanding of how constraints shape the movement solutions of performers in training and competition environments. Analytics can facilitate new insights from numerous and complex data through enhanced non-linear and multivariate analysis techniques. The role of the perceptual sciences is discussed with respect to generating outputs from analytics that are more interpretable for the end-user. Together, these three fields of technology, analytics and perceptual science may enable a more comprehensive understanding of constraints in sports performance.

Phases of match-play in professional Australian Football: Distribution of physical and technical performance

The current study aimed to describe the distribution of physical and technical performance during the different phases of play in professional Australian Football. The phases of play (offence, defence, contested play, umpire stoppages, set shots and goal resets) were manually coded from video footage for a single team competing in 18 matches in the Australian Football League. Measures of physical performance including total distance (m), average speed (m · min-1), low-speed running (LSR, <14.4 km h-1), high-speed running (HSR, >14.4 km h-1), accelerations (2.78 m · s-2) and decelerations (-2.78 m · s-2) were derived from each phase of play via global positioning system (GPS) devices. Technical skill data including tackles, handballs and kicks were obtained from a commercial statistics provider and derived from each phase of play. Linear mixed-effects models and effect sizes were used to assess and reflect the differences in physical and technical performance between the six phases of play. Activity and recovery cycles, defined as periods where the ball was in or out of play were also described using mean and 95% confidence intervals. The analysis showed that several similarities existed between offence and defence for physical performance metrics. Contested play involved the highest total distance, LSR, accelerations, decelerations and tackles compared to all other phases. Offence and defence involved the highest average speed and HSR running distances. Handballs and kicks were highest during offence, while tackles were highest during contested play, followed by defence. Activity and recovery cycles involved mean durations of ~110 and ~39 s and average speeds of ~160 and ~84 m · min-1, respectively. The integration of video, GPS and technical skill data can be used to investigate specific phases of Australian Football match-play and subsequently guide match analysis and training design.

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.

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.

Physical Preparation Factors That Influence Technical and Physical Match Performance in Professional Australian Football

OBJECTIVES

To examine the collective influence of a range of physical preparation elements on selected performance measures during Australian football match play.

DESIGN

Prospective and longitudinal.

METHODS

Data were collected from 34 professional Australian football players from the same club during the 2016 Australian Football League competition season. Match activity profiles and acute (7-d) and chronic (3-wk) training loads were collected using global positioning system devices. Training response was measured by well-being questionnaires completed prior to the main training session each week. Maximal aerobic running speed (MAS) was estimated by a 2-km time trial conducted during preseason. Coach ratings were collected from the senior coach and 4 assistants after each match on a 5-point Likert scale. Player ratings were obtained from a commercial statistics provider. Fifteen matches were analyzed. Linear mixed models were constructed to examine the collective influence of training-related factors on 4 performance measures.

RESULTS

Muscle soreness had a small positive effect (ES: 0.12) on Champion Data rating points. Three-week average high-speed running distance had a small negative effect (ES: 0.14) on coach ratings. MAS had large to moderate positive effects (ES: 0.55 to 0.47) on relative total and high-speed running distances. Acute total and chronic average total running distance had small positive (ES: 0.13) and negative (ES: 0.14) effects on relative total and high-speed running distance performed during matches, respectively.

CONCLUSIONS

MAS should be developed to enhance players' running performance during competition. Monitoring of physical preparation data may assist in reducing injury and illness and increasing player availability but not enhance football performance.