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  • Author: Courtney Sullivan x
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Courtney Sullivan, Thomas Kempton, Patrick Ward and Aaron J. Coutts

Purpose: To develop position-specific career performance trajectories and determine the age of peak performance of professional Australian Football players. Methods: Match performance data (Australian Football League [AFL] Player Rank) were collected for Australian Football players drafted via the AFL National Draft between 1999 and 2015 (N = 207). Players were subdivided into playing positions: forwards (n = 60; age 23 [3] y), defenders (n = 71; age 24 [4] y), midfielders (n = 58; age 24 [4] y), and ruckmen (n = 18; age 24 [3] y). Linear mixed models were fitted to the data to estimate individual career trajectories. Results: Forwards, midfielders, and defenders experienced peak match performance earlier than ruckmen (24–25 vs 27 y). Midfielders demonstrated the greatest between-subjects variability (intercept 0.580, age 0.0286) in comparison with ruckmen, who demonstrated the least variability (intercept 0.112, age 0.005) in AFL Player Rank throughout their careers. Age had the greatest influence on the career trajectory of midfielders (β [SE] = 0.226 [0.025], T = 9.10, P < .01) and the least effect on ruckmen (β [SE] = 0.114 [0.049], T = 2.30, P = .02). Conclusions: Professional Australian Football players peak in match performance between 24 and 27 years of age with age, having the greatest influence on the match performance of midfielders and the least on ruckmen.

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Courtney Sullivan, Johann C. Bilsborough, Michael Cianciosi, Joel Hocking, Justin T. Cordy and Aaron J. Coutts

Objectives:

To determine the physical activity measures and skill-performance characteristics that contribute to coaches’ perception of performance and player performance rank in professional Australian Football (AF).

Design:

Prospective, longitudinal.

Methods:

Physical activity profiles were assessed via microtechnology (GPS and accelerometer) from 40 professional AF players from the same team during 15 Australian Football League games. Skill-performance measure and player-rank scores (Champion Data Rank) were provided by a commercial statistical provider. The physical-performance variables, skill involvements, and individual player performance scores were expressed relative to playing time for each quarter. A stepwise multiple regression was used to examine the contribution of physical activity and skill involvements to coaches’ perception of performance and player rank in AF.

Results:

Stepwise multiple-regression analysis revealed that 42.2% of the variance in coaches’ perception of a player’s performance could be explained by the skill-performance characteristics (player rank/min, effective kicks/min, pressure points/min, handballs/min, and running bounces/min), with a small contribution from physical activity measures (accelerations/min) (adjusted R 2 = .422, F 6,282 = 36.054, P < .001). Multiple regression also revealed that 66.4% of the adjusted variance in player rank could be explained by total disposals/min, effective kicks/min, pressure points/min, kick clangers/min, marks/min, speed (m/min), and peak speed (adjusted R 2 = .664, F 7,281 = 82.289, P < .001). Increased physical activity throughout a match (speed [m/min] β – 0.097 and peak speed β – 0.116) negatively affects player rank in AF.

Conclusions:

Skill performance rather than increased physical activity is more important to coaches’ perception of performance and player rank in professional AF.

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Alexandre Moreira, Johann C. Bilsborough, Courtney J. Sullivan, Michael Cianciosi, Marcelo Saldanha Aoki and Aaron J. Coutts

Purpose:

To examine the training periodization of an elite Australian Football team during different phases of the season.

Methods:

Training-load data were collected during 22 wk of preseason and 23 wk of in-season training. Training load was measured using the session rating of perceived exertion (session-RPE) for all training sessions and matches from 44 professional Australian Football players from the same team. Training intensity was divided into 3 zones based on session-RPE (low, <4; moderate, >4 AU and <7 AU; and high, >7 AU). Training load and intensity were analyzed according to the type of training session completed.

Results:

Higher training load and session duration were undertaken for all types of training sessions during the preseason than in-season (P < .05), with the exception of “other” training (ie, re/prehabilitation training, cross-training, and recovery activities). Training load and intensity were higher during the preseason, with the exception of games, where greater load and intensity were observed during the in-season. The overall distribution of training intensity was similar between phases with the majority of training performed at moderate or high intensity.

Conclusions:

The current findings may allow coaches and scientists to better understand the characteristics of Australian Football periodization, which in turn may aid in developing optimal training programs. The results also indicate that a polarized training-intensity distribution that has been reported in elite endurance athletes does not occur in professional Australian Football.