Possession chain factors influence movement demands in elite Australian football match-play

Vella, Andrew; Clarke, Anthea C.; Kempton, Thomas; Ryan, Samuel; Holden, Jacob; Coutts, Aaron J.

doi:10.1080/24733938.2020.1795235

Cited by 12 publications

(20 citation statements)

References 23 publications

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“…Positional data were synchronised with match event data using the unix timestamps present in both datasets [14], which was used to infer ball position. Field position of the ball was separated into four zones (defensive 50; D50, defensive mid; DM, attacking mid; AM, forward 50; F50) by the two 50 m arcs and the centre of the ground (see Fig 1), which is conventional for AF research and statistical providers [12,15,16]. The impact on match play was determined for three match events-clearances, intercepts, and I50s (entries into the F50), with terminology provided in Table 1.…”

Section: Plos Onementioning

confidence: 99%

Team numerical advantage in Australian rules football: A missing piece of the scoring puzzle?

Alexander

Bedin²,

Jackson³

et al. 2021

PLoS ONE

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The primary aim of this study was to determine the relationship between a team numerical advantage during structured phases of play and match event outcomes in professional Australian football. The secondary aim was to quantify how players occupy different sub-areas of the playing field in match play, while accounting for match phase and ball location. Spatiotemporal player tracking data and play-by-play event data from professional players and teams were collected from the 2019 Australian Football League season played at a single stadium. Logistic regression analysed the relationship between total players and team numerical advantage during clearances and inside 50’s. Total players and team numerical advantage were also quantified continuously throughout a match, which were separated into three match phases (offence, defence, and stoppage) and four field positions (defensive 50, defensive midfield, attacking midfield, and forward 50). Results identified an increased team numerical advantage produced a greater likelihood of gaining possession from clearances or generating a score from inside 50’s. Although, an increased number of total players inside 50 was likely associated with a concomitant decrease in the probability of scoring, irrespective of a team numerical advantage. Teams were largely outnumbered when the ball was in their forward 50 but attained a numerical advantage when the ball was in the defensive 50.

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Section: Plos Onementioning

confidence: 99%

Team numerical advantage in Australian rules football: A missing piece of the scoring puzzle?

Alexander

Bedin²,

Jackson³

et al. 2021

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…These occurrences are potentially heightened during unsuccessful play, as score from turnover has been previously identified as a contributing factor to match outcome [ 35 ]. Furthermore, Vella et al [ 9 ] noted that relative high-speed running distances were greatest when defensive phases began with an intercept, adding further evidence to this theory. Conversely, acceleration and deceleration efforts were greater during successful defensive plays for midfielders and forwards.…”

Section: Discussionmentioning

confidence: 98%

“…However, little data exists in the literature to guide the intensity prescription of these specific training drills in AF. Vella et al [ 9 ] demonstrated differences in distance and high-speed running during specific phases of play (e.g., offence) depending upon where the phase started (e.g., forward-50). However, this study only focused on where the phase began, and did not consider the end field location, which is problematic when translating this data to training [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

“…Vella et al [ 9 ] demonstrated differences in distance and high-speed running during specific phases of play (e.g., offence) depending upon where the phase started (e.g., forward-50). However, this study only focused on where the phase began, and did not consider the end field location, which is problematic when translating this data to training [ 9 ]. Therefore, it appears prudent to assess the demands of the different phases of play based upon the field locations they are played within.…”

Section: Introductionmentioning

confidence: 99%

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Physical and technical demands of offence, defence, and contested phases of play in Australian Football

Wing

Hart

Ma’ayah

et al. 2022

BMC Sports Sci Med Rehabil

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Background This study compared the physical demands and effect of field location for different phases of play (offence, defence and contested), and examined the physical and technical demands of successful and unsuccessful phases of play during Australian Football matches. Methods Global positioning system (GPS) and technical performance data were collected from 32 male Australian Football players in one club over 19 games in the 2019 season. The GPS data was aligned with phases of play acquired using Champion Data. Linear mixed models were used to detect differences between phases of play and field location which were further contextualized using Cohen’s d effect size. Results Physical demands were greatest (p < 0.001) in defensive phases for backs (ES 0.61 to 1.42), and offensive phases for midfielders (ES 0.65 to 0.96) and forwards (ES 0.84 to 1.94). Additionally, distance and high-speed running were lowest in contested phases irrespective of playing position. Distance and high-speed running were greatest in larger field locations (e.g., full ground). No pattern was evident for accelerations or decelerations. Successful offensive plays demonstrated greater physical and technical outputs for midfielders and forwards, whereas the opposite was found for backs. Physical output was largely greater in unsuccessful defensive plays for all positions; however, the rate of tackles and marks was greater during successful defence. Conclusion These findings enable a greater understanding of the demands of Australian Football matches, and can be utilized to inform both representative training design, and the evaluation of player performance.

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“…Despite the added value of the rolling time frame method, the use of non-uniform analysis windows, such as the ball in play (BiP) method, where the analysis period is defined by the natural stop and start of match play, may be able to provide a more detailed description of maximum running periods [ 11 , 16 , 17 ]. The BiP method appears particularly suited to identifying maximum phases in AF, as previous research has demonstrated that the inclusion of data when the ball is out of play reduces relative running performance [ 18 , 19 ]. Although mean running intensities during BiP in AF have been previously demonstrated [ 18 ], the BiP method is yet to be utilised to identify periods of maximum intensity running.…”

Section: Introductionmentioning

confidence: 99%

Physical and technical demands of Australian football: an analysis of maximum ball in play periods

Wing

Hart

Ma’ayah

et al. 2022

BMC Sports Sci Med Rehabil

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Background This study compares ball in play (BiP) analyses and both whole game (WG) and quarter averaged data for physical and technical demands of sub-elite Australian football (AF) players competing in the West Australian Football League across playing positions. Methods Microsensor data were collected from 33 male AF players in one club over 19 games of the 2019 season. BiP time periods and technical performance data (e.g., kicks) were acquired from the Champion Data timeline of statistics, and time matched to the microsensor data. Linear mixed modelling was utilised to establish differences between maximum BiP periods and averaged data. Results The analyses indicated significant differences (p < 0.0001) between maximum BiP and WG data for all metrics and all playing position (half-line, key position, and midfielders). The percentage difference was greatest for very high-speed running (171–178%), accelerations (136–142%), high-intensity efforts (128–139%), and high-speed running (134–147%) compared to PlayerLoad™ (50–56%) and total running distance (56–59%). No significant (p > 0.05) differences were evident for maximum BiP periods when they were compared between playing positions (i.e., half line vs key position vs midfield). Significant (p < 0.0001) differences were also noted between maximum BiP phases and averaged data across all 4 quarters, for each microsensor metric, and all playing positions. Technical actions (e.g., kicks and handballs) were observed in 21–48% of maximum BiP phases, depending on playing positions and microsensor metric assessed, with kicks and handballs constituting > 50% of all actions performed. Conclusions These results show the BiP analysis method provides a more accurate assessment of the physical demands and technical actions performed by AF players, which are underestimated when using averaged data. The data presented in this study may be used to inform the design and monitoring of representative practice, ensuring that athletes are prepared for both the physical and technical demands of the most demanding passages of play.

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Possession chain factors influence movement demands in elite Australian football match-play

Cited by 12 publications

References 23 publications

Team numerical advantage in Australian rules football: A missing piece of the scoring puzzle?

Team numerical advantage in Australian rules football: A missing piece of the scoring puzzle?

Physical and technical demands of offence, defence, and contested phases of play in Australian Football

Physical and technical demands of Australian football: an analysis of maximum ball in play periods

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