https://orcid.org/0000-0002-5689-7801
ABSTRACT
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.
Introduction
Australian football (AF) is one of the most viewed and participated-in sports in Australia (Gray and Jenkins Citation2010), of which the Australian Football League (AFL) is the highest level of competition for male athletes. As of 2022, 18 teams compete in a 23-round season, plus an additional finals series where the top eight teams compete to win the premiership. Elite male AF gameplay consists of four 20-min quarters (plus time on) and features two teams of 23 players competing on an oval shaped field. Eighteen players from each team are on the field at one time (Johnston et al. Citation2018), and there are four interchange positions (plus one medical substitute) for clubs to utilise throughout the match. Each club is permitted 75-interchanges per game, which is a reduction from 90-interchanges allowed in prior seasons.
AF gameplay is intermittent in nature and requires a multi-faceted skillset (e.g. technical, tactical, physical, etc.), with players performing several bouts of high-intensity work interspersed with low-intensity activities; thus, a superior aerobic base and ability to perform anaerobic work intermittently are critical at the elite level (Gray and Jenkins Citation2010). As a result of the introduction of global positioning system (GPS) technology into the AFL in 2005 (Gray and Jenkins Citation2010), the physical demands of AF gameplay have been quantified and reported. On average, elite male AF players complete between 11 and 13 km (Johnston et al. Citation2018), at a rate of 131 ± 17 m.min−1 including 18.8 ± 9.1 m.min−1 of high-speed running (HSR) (>18 km.h−1) (Esmaeili et al. Citation2020) within a match. However, there is wide variability in these demands given that there is a myriad of potential constraints to AF gameplay (e.g. technical, tactical, environmental, positional, location of venue [i.e. home vs away], opposition quality, match outcome [i.e. winning vs losing], etc), which may in-turn contextualise the running demands required to compete. These constraints are commonly referred to as ‘contextual factors’, and are defined by Boers et al. (Citation2019) as a variable associated with the interpretation of results, yet are not the primary objective of gameplay. Contextual factors relating to running demands have been heavily investigated in field-based invasion sports, such as soccer (Trewin et al. Citation2017; Palucci Vieira et al. Citation2019; Hands and Janse de Jonge Citation2020) and rugby (Hausler et al. Citation2016; Dalton-Barron et al. Citation2020).
Despite there being relatively fewer AF-focussed studies in this area, a small number of studies indicate there may be multiple contextual factors that are associated with running demands in AF gameplay, indeed a recent narrative review of running demands in elite male AF (Wing et al. Citation2021) identified numerous contextual factors, which may be associated, but no reviews taking a systematic approach to searching and evaluating relevant articles have been performed. Understanding contextual factors and their association with running demands in elite AF gameplay is important as they provide high-performance sport science practitioners greater awareness of factors that may either enhance or inhibit running performance, along with knowledge in preparing an optimal pre-season program for their players. The purpose of this scoping review was to systematically establish and summarise the contextual factors associated with running demands in elite male AF gameplay that have been reported in the literature, while identifying gaps for future research.
Methods
Search strategy
The review was conducted and reported according to the PRISMA Extension for Scoping Reviews (Peters et al. Citation2020). The objective of this study was to map the extent, range, and nature of the literature in this area, while summarising heterogeneous findings (Tricco et al. Citation2018), thus a scoping review was most appropriate. A systematic search was conducted on the 7th of September, 2021. The following databases were searched: Scopus, SPORTDiscus, Ovid Medline and CINAHL. Database update alerts were monitored until the 10th of August 2022, for any additional articles that met the inclusion criteria. Database searches were complemented with pearling of reference lists of all included articles. The search strategy was guided by previously published systematic reviews (Palucci Vieira et al. Citation2019; Dalton-Barron et al. Citation2020), with input from an academic librarian from the University of South Australia as recommended by Sampson et al. (Citation2009). The search strategy was constructed around Australian Football AND Running Demands AND Contextual Factors (see Supplementary list for detailed search strategy).
Selection criteria
Two reviewers independently screened the articles, with any conflicts being resolved through discussion or via third reviewer. Eligible studies met the following criteria: (1) written in the English language; (2) published no earlier than 2010 (due to advances in GPS technology and rule changes); (3) quantitative in nature; (4) included elite male AF athletes (i.e. players from the AFL); (5) utilised GPS technology to measure match running demands; and (6) performed an analysis identifying contextual factors on match running demands. Studies were excluded if athletes were representative of women’s or sub-elite AF competition, including state-level, recreational, youth and pre-season competitions. Additionally, studies not utilising GPS technology, and non-peer reviewed, qualitative and review articles were also excluded. All identified review articles that were deemed relative to the topic were pearled for additional articles.
Data extraction
After the removal of duplicates via program and manual exclusion relevant studies were extracted by two independent authors. Where possible, the following data were extracted: author(s), year of publication, sample size (i.e. number of participants, matches and GPS files), AFL season(s) analysed, GPS model(s) utilised, coefficient of variation for GPS model, satellite count, horizontal dilution of precision (HDOP), win-loss record of team(s) involved, speed zone threshold(s), definition of contextual factors and/or GPS variables, any data excluded from analysis, and all outcomes relating to contextual factors and/or GPS variables.
Data synthesis
Due to the primary aim of this scoping review being to establish and summarise the contextual factors associated with running demands in elite AF gameplay that have been reported in the literature (and identifying gaps for future research), no formal analysis was carried out. Instead, results such as the number of studies and key findings were described through narrative synthesis. The key outcomes and data from the literature were summarised as mean ± standard deviation (SD) where appropriate.
Results
Search and selection of studies
Database searching yielded a total of 2517 papers, with the removal of duplicates identifying 1391 unique records. Following title and abstract screening, 46 potentially relevant papers underwent full-text screening which resulted in 13 articles being excluded while three additional articles were identified during database monitoring, resulting in 36 articles eligible for data extraction (). Among these studies, 20 unique contextual factors were identified when reporting running demands.
Figure 1. Flowchart of selection process of eligible studies for scoping review.
The identified contextual factors were arranged into five categories: individual, fatigue, game factors, fixture and training history (). Categories were agreed upon via consensus with input from the academic team and practitioners associated with the project. Each category displayed a range of sample sizes, GPS sampling frequency, and publication dates. Refer to supplementary table S1 for complete findings extracted from these studies.
Table 1. Contextual factor categories.
Individual
Position
Thirteen studies investigated playing position as a contextual factor on running demands. Eight studies indicated that nomadic position players (i.e., midfielders, small backs/forwards) had the greatest relative total distance, HSR and Player Load™ (PL; sum of accelerations across x, y and z axes measured via an accelerometer (Boyd et al. Citation2011)) in elite AF gameplay (Wisbey et al. Citation2010; Hiscock et al. Citation2012; Johnston et al. Citation2015, Citation2019; Delaney et al. Citation2017; Esmaeili et al. Citation2020), and in particular midfielders (Brewer et al. Citation2010; Coutts et al. Citation2015; Black et al. Citation2016). Conversely, key position players, such as rucks, tall forwards and tall backs obtained the lowest values for assessed metrics (Wisbey et al. Citation2010; Brewer et al. Citation2010; Hiscock et al. Citation2012; Johnston et al. Citation2015, Citation2019; Coutts et al. Citation2015; Black et al. Citation2016; Delaney et al. Citation2017; Esmaeili et al. Citation2020).
Player rank
Six studies investigated the association between player ranking (as defined by ChampionData® Player Rankings, ChampionData® Pressure Points or subjective ‘coach votes’ awarded by team coaching staff based on how well they felt a player performed during gameplay) and running output. Two studies agreed that players who scored high on coach voting completed greater running output compared to their lower scoring counterparts (Bauer et al. Citation2015; Johnston et al. Citation2016), while one study found the opposite (Johnston et al. Citation2012). Other findings suggest that acceleration load and distance covered above aerobic threshold had a positive relationship with ChampionData® Player Rankings (official ranking system of the AFL measuring player performance using statistical measures (ChampionData Citation2019)) and Pressure Points (Hiscock et al. Citation2012), but relative total distance (i.e. m/min) had mixed results (Hiscock et al. Citation2012; Sullivan et al. Citation2014).
Aerobic fitness
Five studies investigated the association between individual aerobic fitness, measured via the Yo-Yo Intermittent Recovery Test 2 or a 2-km time-trial, and match running demands. All studies agreed that aerobic fitness is positively related to relative total distance covered (Mooney et al. Citation2011, Citation2013; Ryan et al. Citation2017, Citation2018; Dillon et al. Citation2018). Similarly, four studies found aerobic fitness to positively correlate with relative HSR (Mooney et al. Citation2011, Citation2013; Ryan et al. Citation2017, Citation2018).
Involvements
Five studies investigated the association between player involvement (i.e. number of disposals, and offensive and defensive actions) and running demands. Three studies agreed that disposal number positively relates to relative total distance (Hiscock et al. Citation2012; Corbett et al. Citation2017; Dillon et al. Citation2018), while another study found negative relationships between number of involvements and relative total distance (Johnston et al. Citation2019). Further, Hiscock et al. (Citation2012) concluded that disposal number was positively related to distance covered above aerobic threshold specifically, while Corbett et al. (Citation2017) found that HSR had a negative relationship with number of involvements. A final study indicated that when a player is directly involved in either the attacking or defensive chain their relative total distance and HSR is increased within that passage of play (Vella et al. Citation2022).
Experience
Three studies investigated the association between playing experience and running output. Two studies agreed that experience is negatively associated with both relative total distance and PL™ (Hiscock et al. Citation2012; Esmaeili et al. Citation2020), while a third study found no difference between experience level for all measured parameters (Black et al. Citation2016).
Anthropometry
One study investigated the association between individual anthropometry and running demands. Specifically, Esmaeili et al. (Citation2020) concluded a higher body mass (kg) had a negative association with relative HSR, however there was no association with relative total distance or PLTM.
Subjective wellness
One study investigated the contextualisation of subjective wellness on running output, indicating that wellness metrics (i.e. perception of mood, energy, stress, leg heaviness, muscle soreness, sleep quality, hours slept and total wellness) did not correlate with running demands (Bellinger et al. Citation2020).
Fatigue
Time in game
Nine studies investigated the association between time within a game and running demands. There was general agreement that relative total distance reduced as the game prolonged, whether it was comparing subsequent quarters (Coutts et al. Citation2010, Citation2015; Hiscock et al. Citation2012; Black et al. Citation2016; Esmaeili et al. Citation2020), quarter one with quarter four (Hiscock et al. Citation2012; Mooney et al. Citation2013; Gronow et al. Citation2014; Esmaeili et al. Citation2020) or the first half with the second half (Brewer et al. Citation2010; Mooney et al. Citation2013). Similarly, HSR reduced as game time prolonged in most studies (Brewer et al. Citation2010; Coutts et al. Citation2010, Citation2015; Hiscock et al. Citation2012; Mooney et al. Citation2013; Gronow et al. Citation2014; Esmaeili et al. Citation2020), although Sheehan et al. (Citation2022). found a positive correlation between total distance (as a surrogate measure of match time) and HSR.
Rotations
Seven studies investigated the association between rotations and match running demands. Among these studies, the association between match running demands and rotations has been sub-categorised to stint duration (i.e. time on field) (Montgomery and Wisbey Citation2016; Dillon et al. Citation2018; Esmaeili et al. Citation2020), recovery duration (Esmaeili et al. Citation2020), the comparison of subsequent stints (Aughey Citation2010; Esmaeili et al. Citation2020), and the number of rotations (Wisbey et al. Citation2010; Mooney et al. Citation2013; Ryan et al. Citation2017; Dillon et al. Citation2018). There seemed to be agreement that a larger stint duration reduced total gameplay running output (Montgomery and Wisbey Citation2016; Dillon et al. Citation2018; Esmaeili et al. Citation2020), while the number of rotations had a positive relationship with running demands (Wisbey et al. Citation2010; Mooney et al. Citation2013; Ryan et al. Citation2017).
Neuromuscular fatigue
One study investigated the direct association between neuromuscular fatigue and running demands (Cormack et al. Citation2013). The study indicated that neuromuscular fatigue measured via flight time to contact time ratio led to changes in x, y and z vector loads (i.e. accelerometer load in the mediolateral, anteroposterior and vertical planes) when adjusted for load per minute, relative total distance and HSR in both a fatigued and non-fatigued state.
Game factors
Phases of play
Eight studies investigated phases of play such as when in possession, stoppages, set shots, kick-ins, intercepts, goal reset, contested play, and attacking and defensive chains. Five studies investigated the association between stoppages and running demands. In elite AF, a stoppage is classified when the ball is pronounced ‘dead’, which occurs during a ball-up, boundary throw in or between goal scoring (Rennie et al. Citation2020). All studies agreed stoppages had a negative association with relative total distance (Ryan et al. Citation2017; Dillon et al. Citation2018; Rennie et al. Citation2020; Vella et al. Citation2020, Citation2022). There was also agreeance that both attacking and defensive chains produced similar activity profiles (Rennie et al. Citation2020; Sheehan et al. Citation2021). However, Vella et al. (Citation2022) showed that running demands in attacking and defensive chains were related to the number of players involved in the respective chain. Specifically, running demands in attacking chains were reduced for each additional player involved, but were increased in defensive chains for each additional player involved.
Match outcome
Four studies investigated the association between match outcome and running demands. Two studies investigated running metrics in a win versus loss context, with one finding an increase in both relative total distance and HSR in matches won (Ryan et al. Citation2017), and the other had no correlation (Esmaeili et al. Citation2020). Other studies investigated running demands with regard to quarter outcome; one study concluded that running output increases in a losing quarter (Sullivan et al. Citation2014), while the final study concluded that the strongest predictors for winning quarters were the time without possession spent in HSR and very high-speed running (Gronow et al. Citation2014).
Margin
Three studies investigated the association between margin and running output. Two studies found an inverse relationship between margin and relative total distance (Hiscock et al. Citation2012; Sullivan et al. Citation2014), HSR and PL (Sullivan et al. Citation2014). Conversely, Esmaeili et al. (Citation2020) concluded that the final margin had a trivial association with relative total distance, HSR and PLTM.
Team ranking
Two studies investigated the association between team ranking and running demands. One study concluded that superior teams (as defined by end of season ranking) have both increased relative HSR and accelerations (Delaney et al. Citation2017), while Ryan et al. (Citation2017) similarly found that competing against superior teams increased relative total distance of the opposition team in comparison to opposing weak teams.
Fixture
Season phase
Five studies investigated the relationship between season phase (i.e. start, end or finals) and running metrics. Two studies evaluated running demands in the minor rounds compared to the finals series. Aughey (Citation2011) concluded that finals matches increased the running output among players, while in contrast, Esmaeili et al. (Citation2020) concluded that finals matches reduced relative total distance, HSR and PLTM. Relative total distance (Ryan et al. Citation2017; Johnston et al. Citation2019), HSR (Kempton et al. Citation2015; Johnston et al. Citation2019), the number of efforts, and distance covered sprinting (Kempton et al. Citation2015) were shown to increase as the season progressed in three out of three studies (Kempton et al. Citation2015; Ryan et al. Citation2017; Johnston et al. Citation2019).
Match location
Four studies investigated the association between match location (i.e. home or away, and venue) and running demands. Two studies compared home versus away matches, with one finding that away games produced greater distance above the individual aerobic threshold (Hiscock et al. Citation2012), and the other finding no difference among any speed zone (Routledge et al. Citation2020). Wisbey et al. (Citation2010) investigated the running demands at five different venues, and found that different venues produced differences in relative and absolute total distance, but not the number of accelerations.
Days break between fixtures
Three studies investigated the association between days break between games and running demands. Two studies found no correlation with relative total distance, HSR, or PLTM between a ‘short’ (i.e. ≤6 days) and ‘long’ (i.e. ≥7 days) break (Ryan et al. Citation2017; Esmaeili et al. Citation2020), with the third study concluding that a ‘longer’ break (i.e. 12 days) lead to greater relative total distance compared with ‘shorter’ breaks (i.e. 6 and 8 days) (Hiscock et al. Citation2012).
Travel
Two studies investigated the association between travel and gameplay running metrics. One study indicated matches played post-travel (interstate) had lower relative total distance and HSR than games not preceded by travel (Ryan et al. Citation2017). The second study concluded that neither travelling in the previous nor current round had a correlation with relative total distance, HSR or PLTM (Esmaeili et al. Citation2020).
Playing conditions
Two studies investigated the association between playing conditions (i.e. weather and time of day) and running demands. Esmaeili et al. (Citation2020) suggested moderate rainfall and high apparent temperatures (i.e., a function of ambient temperature, humidity and wind speed (Steadman Citation1994)) reduced running output. Conversely, Hiscock et al. (Citation2012) concluded wet games had no association with relative total distance, HSR or accelerations.
Training history
Training history was identified as a standalone factor within this review. Two studies investigated the association between training history and running output, with one study indicating a ‘high’ pre-season training load group completed greater gameplay relative total distance and HSR than both ‘moderate’ and ‘low’ pre-season training load groups (Johnston et al. Citation2019). The additional study revealed accumulated distance in-season had a negative association with both relative total distance and HSR during gameplay (Ryan et al. Citation2018).
Discussion
The aim of this scoping review was to establish and summarise the contextual factors associated with running demands in elite AF gameplay that have been reported in the literature, while identifying gaps for future research. The review illustrates there are many factors which correlate with running demands in elite AF; 20 contextual factors were identified from 36 studies. The main findings from the review indicate playing position, aerobic fitness, rotations, time within a game, stoppages, and the phase within season all correlate with running demands in elite AF gameplay. These findings can support high-performance practitioners understand the running demands their players will encounter in various contexts, allowing them to condition players accordingly to ensure optimal preparation. However, it is also clear that many contextual factors are understudied, and thus this review is challenged by a small number of studies investigating certain factors, and consequently an inability to make unequivocal conclusions.
The most prominently researched contextual factor was playing position. There was a consensus among the studies that playing position is related to running output in elite AF gameplay. Specifically, nomadic position players produced greater running output in comparison to key position players (i.e. 4.6% to 9.1% greater total distance and 15.6% to 47.9% greater HSR) (Wisbey et al. Citation2010; Brewer et al. Citation2010; Hiscock et al. Citation2012; Johnston et al. Citation2015, Citation2019; Coutts et al. Citation2015; Black et al. Citation2016; Delaney et al. Citation2017; Esmaeili et al. Citation2020). This was an intuitive finding due to the tactical constraints of key position players minimising their physical output in comparison to nomadic position players (Mooney et al. Citation2011). Additionally, nomadic position players tend to rotate more frequently than key position players thus allowing them to maintain high-intensity work (Wisbey et al. Citation2010). These results are important as they provide an objective reflection of the differences in game demands between individual positions, thus potentially enhancing athlete preparation and monitoring techniques used by practitioners when considering players of differing positions.
The AFL pre-season is one of the longest of its kind in the world, and since aerobic fitness has been shown to aid recovery during high-intensity intermittent exercise (Tomlin and Wenger Citation2001), a focus on the development of a strong aerobic base to withstand the high-intensity demands during gameplay is important. Indeed, aerobic fitness is a critical component to elite AF; all included studies in this review indicated a positive relationship between end of pre-season aerobic fitness and relative total distance (Mooney et al. Citation2011, Citation2013; Ryan et al. Citation2017, Citation2018; Dillon et al. Citation2018), while four studies also found a positive correlation with relative HSR (Mooney et al. Citation2011, Citation2013; Ryan et al. Citation2017, Citation2018). However, as mentioned previously, there is a lack of research regarding training history as a contextual factor on gameplay running demands. Only one study has compared pre-season running volumes, in-which the ‘high’ training load group completed greater relative total distance and HSR in gameplay than the ‘moderate’ and ‘low’ training load groups (Johnston et al. Citation2019). Taken together, the results for aerobic fitness and training history show the importance of developing aerobic fitness through conditioning (potentially as part of pre-season training) as it may lead to increased running output during gameplay, which is beneficial as this study states positive correlations exist between running output and each of player rank (Hiscock et al. Citation2012; Bauer et al. Citation2015; Johnston et al. Citation2016), and involvements (Hiscock et al. Citation2012; Corbett et al. Citation2017; Dillon et al. Citation2018). This finding may be further enhanced by increased knowledge on the association with training history and may benefit high-performance staff to optimise pre-season planning.
Rotations are a critical component to the game of AF. Throughout the years, the AFL has introduced several rule changes regarding the use of rotations in an attempt to alter the flow of gameplay, as explained by Hocking (Citation2020): ‘The main reason (for the 75-interchange cap) is to try and open up congestion around the ground’. There was a consensus among studies that the number of rotations accumulated by a player correlated with greater relative total distance (Wisbey et al. Citation2010; Mooney et al. Citation2013; Ryan et al. Citation2017) and HSR (Mooney et al. Citation2013; Ryan et al. Citation2017), while these values also reduced in subsequent stints on the field (Aughey Citation2010; Esmaeili et al. Citation2020). Therefore, these results suggest the rule changes implemented by the AFL may potentially reduce a players ability to cover the ground and attend repeat stoppages, leading to less congestion (i.e. high density of players in a small area (Wallace and Norton Citation2013)) around the ball. A further finding within these studies were a game total of eight rotations for an individual player has little to no further association with their running output, while six rotations seems to be the minimum required to maintain a high-running capacity (Montgomery and Wisbey Citation2016). However, with the current restriction of 75-interchanges in elite AF, it is not feasible for all players to seek 6–8 rotations per game, which is further evidence the rule changes implemented by the AFL may reduce congestion around the ground. The authors of the present review hypothesise that a greater number of rotations would enhance an individual’s ability to perform high-intensity work, since passive recovery on the interchange bench allows heart rate to decline (Moss and Twist Citation2015), exercise by-products to be processed (Soetanto et al. Citation2019) and for nutritional ergogenic aids to be ingested (Cermak and van Loon Citation2013), thus leading to improved running capacity (Clarke et al. Citation2019).
Game factors are important to consider when contextualising running demands. As in many sports, elite AF is unpredictable, and skill execution and physical output of players are influenced by the events occurring within the 80 min of gameplay (Johnston et al. Citation2018). Phases of play are a common topic in game analysis, but often overlooked when analysing the physical demands of the game. Rennie et al. (Citation2018) state that umpire stoppages take up 18% of total game time, while the studies within this review indicate that a greater number of stoppages reduced relative total distance (Ryan et al. Citation2017; Dillon et al. Citation2018; Rennie et al. Citation2020; Vella et al. Citation2020, Citation2022), although HSR was unaffected (Ryan et al. Citation2017; Dillon et al. Citation2018). During the 2005–2009 period, possession based football was the preferred strategy (Woods et al. Citation2017); however, since the 2010 season there has been a drastic tactical change seeing teams aggressively attempt to re-possess the football from their opposition, leading to greater congestion and number of stoppages in the modern-game (Woods et al. Citation2017). High-performance practitioners can utilise these data to alter running output during the design of small-sided games throughout training.
When evaluating an elite AF season as a continuum, the present review found an increase in running metrics as the season progressed (Kempton et al. Citation2015; Ryan et al. Citation2017; Johnston et al. Citation2019). Given that higher apparent temperatures reduced running output (Esmaeili et al. Citation2020), and that the AFL season starts in Autumn (when temperatures are warm to mild) and progress into Winter (when temperatures are quite cold), players may strategically pace themselves to ensure they are able to finish games (Armstrong et al. Citation2007). Practitioners may anecdotally believe running is greatest at the start of the season when players are ‘freshest’ and ‘fittest’, and this diminishes as the season progresses due to the lower physiological demands of in-season training which, when coupled with accumulated fatigue from gameplay, may reduce overall player readiness to perform compared to the start of the season. However, this does not appear to be the case based on the included studies.
There are many contextual factors that are understudied regarding their association with running demands in elite AF gameplay. Some of these factors are team ranking, travel, playing conditions and days break. Additionally, these factors are outside the control of high-performance practitioners and players, and thus greater understanding of their correlation with gameplay running demands would be of importance to enhance knowledge and improve the preparation and recovery of players. For example, contextual factors can be applied to gain greater interpretation of a players’ physiological status. Running output alone can often be an indicator of the physiological status of a player, however as the present study has indicated, there are several contextual factors that may correlate with running demands. Thus, it is important that practitioners can differentiate which changes in running output may be a result of contextual factors, and which changes may be a true reflection of physiological status.
Limitations and future direction
Findings illustrated within this review may be limited due to several factors involving the content of the selected studies. Firstly, there is not a universal threshold regarding the definition of HSR or other speed zones, and thus studies throughout this review utilised differing definitions. Studies throughout this review also utilised a variety of GPS systems which consisted of multiple brands, models, and sampling rates. Given the variability in speed zone thresholds and GPS technology observed in this review, comparison between studies is difficult. Secondly, numerous studies included within this review conducted their investigation with an overlapping combination of several factors, and may thus not be a true indication of the association of that factor in isolation. Further studies investigating contextual factors in isolation may be warranted. Additionally, studies from Dillon et al. (Citation2018) and Ryan et al. (Citation2018, Citation2017) utilised the same samples in their studies investigating the association of aerobic fitness with running demands, as did Vella et al. (Citation2022, Citation2020) in their studies investigating the association of phases of play with running demands. Thus, there may be some biases regarding the association between aerobic fitness and phases of play on gameplay running demands. Lastly, most studies included in this review only obtain a sample size from one team, therefore these results may not be generalised amongst the AFL. This review has identified several gaps in the literature regarding the investigation of contextual factors associated with running demands in elite AF, such as a lack of AF specific factors. Some discrete events have been studied (e.g. player involvement, match score, phases of play etc.), however contextual factors such as contested/uncontested marks, tackles, goals scored and handball receives have not been studied, which would be beneficial in gaining greater understanding of the association of contextual factors with running demands in elite AF. Furthermore, many factors within the review contain a limited number of studies and/or contain conflicting findings, and thus results from the review are not clear. Future research should attempt to reach a consensus in speed zone thresholds, such that consistent thresholds can be applied in future studies.
Conclusion
This scoping review identified 36 studies examining the contextual factors associated with running demands in elite AF. Twenty contextual factors were found and grouped by the authors into five main categories, however, it is evident that contextual factors associated with running demands in elite AF are under-researched in comparison to other footballing codes (Hausler et al. Citation2016; Trewin et al. Citation2017; Palucci Vieira et al. Citation2019; Hands and Janse de Jonge Citation2020; Dalton-Barron et al. Citation2020). There is limited evidence for many contextual factors, either due to a lack of research, or due to equivocal findings across included studies. Further research is needed to help elucidate what factors associate upon running demands, however it seems playing position, aerobic fitness, rotations, time within a game, stoppages and season phase are all important factors. Understanding contextual factors and their association with running demands in elite AF is important and should be utilied by high-performance practitioners due to the potential of enhancing individual player monitoring techniques and workload planning with a view to optimising physical performance.
Author contribution
Article Design: Josh Gregorace, Grace Greenham, Clint Bellenger, Max Nelson; Literature Search: Josh Gregorace, Ashleigh Edwards; Data Analysis: Josh Gregorace; Writing: Josh Gregorace; Drafting: Clint Bellenger, Max Nelson, Grace Greenham, Ashleigh Edwards
Disclosure statement
No potential conflict of interest was reported by the authors.
Data availability statement
Data sharing is not applicable to this article as no new data were created or analysed in this study.
Additional information
Funding
References
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