Australian football (AF) is, arguably, Australia’s major football code with high participation at numerous levels ranging from young children and junior levels, through to amateur senior leagues and at the top level, the professional Australian Football League. Similar to other sports, the opportunities to reach the professional level diminish considerably with many dropping out due to injuries and de-selection, as well as motivation, study and time conflicts with other activities.

Over the past decade, it is commonly accepted that AF has progressed into a fast paced sport characterised by high intensity play periods and longer rest intervals (Norton et al., 1999). As a result, coaches and sports scientists have increased their attention and focus on determining the game-related physical demands with a view to maximising the training and physical preparation of each athlete. Consequently, time-motion analysis (a component of notational or performance analysis) has developed into an important tool for use when investigating movement patterns and the occurrence of specific game-related skills with a view to increasing the understanding and knowledge of the demands of this sport. Time-motion analysis has been widely used across a variety of major football codes (Kay and Gill, 2003; Reilly and Thomas, 1976) as a tool for analysis of specific players and/or their positions. However, its use in AF to assess game demands is sparse.

Early analyses in AF conducted by Jacques and Pavia, 1974, Hahn et al., 1979 and McKenna et al., 1988 are now considered outdated, as a direct result of the sports progression in both rules and standards of play. However, more recent analyses by Dawson et al. (2004a; 2004b) have presented contemporary data on the physical and movement patterns in professional AF players.

Presently, the research available on game demands in AF has focused exclusively on the elite senior competition. Subsequently, there is an absence of analysis conducted at the elite junior Under 18 (U/18) competition level. Coaching staff at the elite junior level must attempt to make decisions regarding training regimes and performance expectations from non-age specific data, taking into account the development and maturational differences in their players when adapting the data collected on the professional population. As this level of competition is a precursor to professional AF, it is essential to study the differences in game demands and movement patterns between the two elite playing levels so the data obtained can be used to appropriately develop athletes to improve their performance. Therefore, as no study has been presented on the movement patterns at this level of AF performance, the aim of this exploratory study was to investigate any observable differences in the movement patterns and game demands between elite junior and senior athletes participating in AF.

Approval of the study, conforming to the Code of Ethics of the World Medical Association (Declaration of Helsinki), was granted by the Board of Management and Coaching Panel, Western Jets Football Club, which participates in the Victorian U/18 year competition. Ethics approval was granted by the Human Ethics Committee of Victoria University, Melbourne, Australia.

Nine U/18 AF athletes (17.3 ± 0.7 years) participated in the study and were tracked over five randomly chosen games throughout the 18 games of the regular 2005 season period (March to September). Prior intention to record a position was not indicated to any athlete and randomly chosen by the head coach certifying that data collection was “single blind”.

Figure 1 illustrates the nomenclature, placement of positions on the field and position descriptions adapted from those used in the study by Dawson et al., 2004a.

Sprinting, running and jogging in any direction were collectively categorised under ‘working’; and walking in any direction was categorised under ‘resting’.

The following procedures were completed throughout the study:

To ensure reliability of measurements, three of the five games were chosen at random for retrospective re-analysis, with movement patterns re-measured to determine intra-observer reliability. As suggested by Hughes et al., 2004 reliability testing was conducted on the raw data sets obtained from the three games. Test-retest reliability was assessed by applying Wilcoxon-Mann-Whitney U Test, and Kendall Tau correlation analysis as the data was not normally distributed. Analysis revealed values for all movement patterns were non-significant and correlations were r = 0.98 or greater which were found to be similar to those reported by Dawson et al., 2004a.

Following game observations, data was logged into an Excel spreadsheet producing a report documenting the movement patterns for the different positions each quarter and across the entire game. Limitations in the collection of data, measuring only time spent resting and walking, did not allow for the delineation between movement intensities such as walking, jogging, running and sprinting within the ‘working’ and ‘resting’ categories. With small numbers of athletes observed (n = 9) for the five positions analysed, no inferential statistics were calculated. Descriptive data were produced with values expressed as mean (± SD) and ranges, and rounded to the nearest whole number.

The results presented are totals of five different playing positions from the one team, each being recorded once during separate junior elite U/18 AFL games. Due to the unexpected requirement of the observer to complete another game-related task, the BP position was tracked during the first three quarters, resulting in an incomplete game data for the BP and will be presented “as is”. Three of the five games were completed on the team’s home ground, whilst two were played at different away venues. Each game was played in dry conditions, and only one game was won by the team involved in this study.

The total distance analysis of each position, including the total game distance and the break down of distances spent ‘working’ (sprinting, running and jogging) and ‘resting’ (walking) are shown in Table 1. The Wing position was recorded to have covered the greatest total distance during a full game. Centre and HBF positions recorded slightly shorter total distance than the Wing but greater than the Ruck position.

Analysis of ‘working’ and ‘resting’ efforts showed the Wing position recorded the greatest total distance covered whilst ‘working’. The Ruck position covered 30% less distance than the Wing position whilst working. Both the HBF and Centre positions covered a greater distance ‘resting’ (7.2% and 6.2% respectively) when compared to the Wing position. Although total distance ‘resting’ was less than the Centre or HBF positions, the Ruck showed a greater percentage of total field position distance covered whilst ‘resting’ than either the Wing, HBF or Centre positions.

Tables 2 and 3 report the number of ‘working’ and ‘resting’ efforts performed during each quarter, showing the average distance, the longest effort and also the distance range per effort. The HBF position completed the greatest number of ‘working’ efforts throughout the entire game, completing 180 efforts. The HBF position also completed the greatest number of efforts whilst ‘resting’, recording 182 efforts. The HBF and Ruck positions recorded more ‘resting’ efforts than ‘working’ efforts, two and five respectively. In accordance with recording the greatest total distance covered whilst ‘working’, the Wing position also recorded the greatest mean distance per effort, covering 58 m per effort (Figure 2).

The quarter by quarter analysis of ‘working’ efforts (Table 2) for each position showed that the Centre, Wing and BP positions recorded the greater number of efforts during the first quarter compared to the second and all positions tracked for the entire four quarters recorded the greater number of ‘working’ efforts in the third quarter compared to the fourth. Mean distance per effort (Table 2) showed increased values in the Ruck, Wing and HBF positions between first and second quarters. After the half-time break the Ruck, Wing and HBF showed decreased mean distance per effort in the third quarter compared to the final quarter.

The quarter by quarter analysis of ‘resting’ efforts (Table 3) revealed the Ruck, Centre and BP positions completed more efforts in the first quarter compared to the second. The Wing and BP recorded the greater mean distance in the first compared to the second quarter and the Ruck and Centre positions the same mean distance during the first two quarters. Of the four positions tracked for the entire four quarters, the Centre was the only position to record more ‘resting’ efforts in the fourth quarter compared to the third and the only position to record a greater mean distance per effort in the third quarter compared to the fourth.

Figure 3 shows the number of ‘working’ efforts completed over various distances throughout each game. All positions show a tendency to complete the greatest number of ‘working’ efforts between 0-39.99 m, with four of the five positions recorded most ‘working’ efforts in the 20-39.99 m category (HBF completed more efforts between 0-19.99 m). Each position showed a decrease in the number of ‘working’ efforts completed over distances greater than 40m. The Ruck position showed the greatest drop in number of working efforts as the distance increased, showing a tendency to complete only 6% of total efforts greater than 80 m.

Figure 4 shows the number of ‘resting’ efforts completed over all distances throughout each game. Similar to the working efforts, all positions showed a tendency to complete the greatest number of efforts over shorter distances (0-20 m), reporting a decrease in the number of efforts over increasing distances.

The purpose of this exploratory study was to observe the movement patterns of five different playing positions involved during a junior elite U/18 game in the 2005 season and compare this initial data to previous research (Dawson et al., 2004a). The underlying aim of the analysis was to assist the appropriate design of specific training programs for this specific U/18 team however; the data may also have general implications to the wider population within this age group. This study is the first to accompany the current limited research available documenting the match-play movement patterns and physical requirements of this AF population group.

It is important to note that methodological limitations in this exploratory study were the type of observation (only recording distances covered) and type recording technique, and the relatively low number of players observed in the study which makes normative profiling difficult (Hughes et al., 2004). . However, the mean results of all players presented from the five matches highlight both similarities and differences in the movement patters observed from the five playing positions chosen during games in this study which suggest further research must continue.

Previous research (Norton et al., 1999; Dawson et al., 2004a) has presented data reporting the movement patterns of elite senior level athletes competing at the national level of competition (Table 4). The data obtained within this study has used an elite junior population group at the relatively early stage of their development within the sport. Consequently, discretion is required, in terms of fitness levels and skill abilities, when comparing the observed differences within the two population groups.

Of the five positions tracked during this study, two positions (Centre and Wing) are classified as a part of a team’s midfield, whilst a third position (HBF) is important in returning the ball out of defence and starting a counter-attack. Coaches may fill these positions with players of similar athletic capabilities involving speed, agility and endurance, highlighted by similar total movement distances recorded. However, there were a number of differences observed between these three positions that could be important and should be considered carefully when determining who might be capable of playing each position and how each position should be adequately prepared.

The HBF position has two roles during a game: (1) limiting the impact of their direct opponent; and (2) providing a key role in linking up the defensive and offensive positions when returning the ball out of defence. During this study, the participants’ team lost the game, indicating a greater workload placed upon the defensive positions, possibly resulting in a greater distance of coverage when compared to a game in which the participant’s team won. This concurs with Reilly and Thomas (1976) who reported the large variation in distance covered by the defensive positions according to the attacking style and line-up of the opposing team. Subsequently, the HBF position recorded the greatest number of ‘working’ efforts during an entire game. As the HBF is often constrained to the defensive half of the ground, it covered less total ‘working’ distance than the midfield positions due to a smaller average distance per effort, being more ‘stop- start’ in nature.

The midfield positions (Wing and Centre) on the other hand, as reported by Dawson et al., 2004a, complete a greater number of game skills and are involved in a greater number of contests than any other position on the ground. Hence, they are required to cover a greater distance per effort and in total to be present at as many contests as possible. The Wing and Centre positions completed more ‘working’ than ‘resting’ efforts throughout the game, suggesting a larger number of ‘working’ efforts were broken by the completion of a skill compared to a true ‘resting’ effort. Skill execution requires the expenditure of energy, therefore decreasing recovery time further as they continued to work to the next contest. Based on this study, planning for training activities involving the midfield players should involve a large number of working efforts with minimal recovery intervals. This could include some working efforts followed by minimal rest perhaps broken only by the inclusion of a skill such as kicking.

The Ruck position, usually the tallest player on the team, is a key component of the team’s midfield, being the first person to contest the ball upon the restart of play. However, tactical innovations in recent years have resulted in positioning the ruckman predominantly through the midfield and defensive regions with a forward positional player taking on the ruckman’s duties in the attacking 50m area. Subsequently, whilst providing his team with a kick to target, the ruckman is often responsible to run to contests through the middle and defensive area of the ground, thus covering a similar number of ‘working’ efforts, although lesser distance compared to the midfield positions. In comparison to the other positions in this study, the Ruck spent a greater percentage of total distance whilst ‘resting’ and a smaller percentage of distance whilst ‘working’. This suggests that training drills for the ruckman involving greater resting intervals between working efforts should be prescribed compared to the midfield positions.

In general, the number of efforts performed was similar across the playing positions, but there were larger differences in the distances covered. These results confirm that fitness sessions should be designed and implemented throughout each training session that focus on repeat efforts over these specific distances required by the playing positions. Whilst some efforts recorded over 150 m and fewer over 200 m, the mean distances recorded during this study were between 30-70 m. Therefore, running sessions should be designed around efforts covering distances of 30-70 m with recovery intervals covering 10-30m, specifically replicating those recorded during this study.

This study examined the physical positional requirements of an elite junior U/18 AF club. The results showed that when compared to recent data at the elite level (Dawson et al., 2004a; Table 4), this preliminary study shows clear delineation in work and rest loads between elite senior and junior U/18 AFL level games. Consequently, this highlights the need for further research into the movement patterns at the U/18 level, including analysis of all positions and more games for normative profiling, the subdivision of ‘working’ and ‘resting’ into more specific categories (for example, walking, jogging, running and sprinting) using time criteria, and game activity demands (for example, specific skills) using technologies such as video analysis, global positioning systems and computer based tracking to provide greater in-depth information.