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  • Author: Dean Ritchie x
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Dean Ritchie, Will G. Hopkins, Martin Buchheit, Justin Cordy and Jonathan D. Bartlett

Purpose:

Load monitoring in Australian football (AF) has been widely adopted, yet team-sport periodization strategies are relatively unknown. The authors aimed to quantify training and competition load across a season in an elite AF team, using rating of perceived exertion (RPE) and GPS tracking.

Methods:

Weekly totals for RPE and GPS loads (including accelerometer data; PlayerLoad) were obtained for 44 players across a full season for each training modality and for competition. General linear mixed models compared mean weekly load between 3 preseason and 4 in-season blocks. Effects were assessed with inferences about magnitudes standardized with between-players SD.

Results:

Total RPE load was most likely greater during preseason, where the majority of load was obtained via skills and conditioning. There was a large reduction in RPE load in the last preseason block. During in-season, half the total load came from games and the remaining half from training, predominantly skills and upper-body weights. Total distance, high-intensity running, and PlayerLoad showed large to very large reductions from preseason to in-season, whereas changes in mean speed were trivial across all blocks. All these effects were clear at the 99% level.

Conclusions:

These data provide useful information about targeted periods of loading and unloading across different stages of a season. The study also provides a framework for further investigation of training periodization in AF teams.

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Dean Ritchie, Will G. Hopkins, Martin Buchheit, Justin Cordy and Jonathan D. Bartlett

Context:

Training volume, intensity, and distribution are important factors during periods of return to play.

Purpose:

To quantify the effect of injury on training load (TL) before and after return to play (RTP) in professional Australian Rules football.

Methods:

Perceived training load (RPE-TL) for 44 players was obtained for all indoor and outdoor training sessions, while field-based training was monitored via GPS (total distance, high-speed running, mean speed). When a player sustained a competition time-loss injury, weekly TL was quantified for 3 wk before and after RTP. General linear mixed models, with inference about magnitudes standardized by between-players SDs, were used to quantify effects of lower- and upper-body injury on TL compared with the team.

Results:

While total RPE-TL was similar to the team 2 wk before RTP, training distribution was different, whereby skills RPE-TL was likely and most likely lower for upper- and lower-body injury, respectively, and most likely replaced with small to very large increases in running and other conditioning load. Weekly total distance and high-speed running were most likely moderately to largely reduced for lower- and upper-body injury until after RTP, at which point total RPE-TL, training distribution, total distance, and high-speed running were similar to the team. Mean speed of field-based training was similar before and after RTP compared with the team.

Conclusions:

Despite injured athletes’ obtaining comparable TLs to uninjured players, training distribution is different until after RTP, indicating the importance of monitoring all types of training that athletes complete.

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Farhan Juhari, Dean Ritchie, Fergus O’Connor, Nathan Pitchford, Matthew Weston, Heidi R. Thornton and Jonathan D. Bartlett

Context: Team-sport training requires the daily manipulation of intensity, duration, and frequency, with preseason training focusing on meeting the demands of in-season competition and training on maintaining fitness. Purpose: To provide information about daily training in Australian football (AF), this study aimed to quantify session intensity, duration, and intensity distribution across different stages of an entire season. Methods: Intensity (session ratings of perceived exertion; CR-10 scale) and duration were collected from 45 professional male AF players for every training session and game. Each session’s rating of perceived exertion was categorized into a corresponding intensity zone, low (<4.0 arbitrary units), moderate (≥4.0 and <7.0), and high (≥7.0), to categorize session intensity. Linear mixed models were constructed to estimate session duration, intensity, and distribution between the 3 preseason and 4 in-season periods. Effects were assessed using linear mixed models and magnitude-based inferences. Results: The distribution of the mean session intensity across the season was 29% low intensity, 57% moderate intensity, and 14% high intensity. While 96% of games were high intensity, 44% and 49% of skills training sessions were low intensity and moderate intensity, respectively. Running had the highest proportion of high-intensity training sessions (27%). Preseason displayed higher training-session intensity (effect size [ES] = 0.29–0.91) and duration (ES = 0.33–1.44), while in-season game intensity (ES = 0.31–0.51) and duration (ES = 0.51–0.82) were higher. Conclusions: By using a cost-effective monitoring tool, this study provides information about the intensity, duration, and intensity distribution of all training types across different phases of a season, thus allowing a greater understanding of the training and competition demands of Australian footballers.

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Fergus O’Connor, Heidi R. Thornton, Dean Ritchie, Jay Anderson, Lindsay Bull, Alex Rigby, Zane Leonard, Steven Stern and Jonathan D. Bartlett

Sprint capacity is an important attribute for team-sport athletes, yet the most appropriate method to analyze it is unclear. Purpose: To examine the relationship between sprint workloads using relative versus absolute thresholds and lower-body soft-tissue and bone-stress injury incidence in professional Australian rules football. Methods: Fifty-three professional Australian rules football athletes’ noncontact soft-tissue and bone-stress lower-body injuries (N = 62) were recorded, and sprint workloads were quantified over ∼18 months using the global positioning system. Sprint volume (m) and exposures (n) were determined using 2 methods: absolute (>24.9 km·h−1) and relative (≥75%, ≥80%, ≥85%, ≥90%, ≥95% of maximal velocity). Relationships between threshold methods and injury incidence were assessed using logistic generalized additive models. Incidence rate ratios and model performances’ area under the curve were reported. Results: Mean (SD) maximal velocity for the group was 31.5 (1.4), range 28.6 to 34.9 km·h−1. In comparing relative and absolute thresholds, 75% maximal velocity equated to ~1.5 km·h−1 below the absolute speed threshold, while 80% and 85% maximal velocity were 0.1 and 1.7 km·h−1 above the absolute speed threshold, respectively. Model area under the curve ranged from 0.48 to 0.61. Very low and very high cumulative sprint loads ≥80% across a 4-week period, when measured relatively, resulted in higher incidence rate ratios (2.54–3.29), than absolute thresholds (1.18–1.58). Discussion: Monitoring sprinting volume relative to an athlete’s maximal velocity should be incorporated into athlete monitoring systems. Specifically, quantifying the distance covered at >80% maximal velocity will ensure greater accuracy in determining sprint workloads and associated injury risk.