Monitoring athlete preparedness in professional Australian football: Load, self-report measures and performance

Monitoring athlete preparedness, including quantifying training and competition load and determining fatigue/training status, is used to complement training and recovery prescription in professional sport (Kenttä & Hassmén, 2002). The overall objective of this research was to investigate contemporary athlete monitoring practices in professional Australian football (AF). The aim of study 1 was to identify the relationship between external training load and session rating of perceived exertion (s-RPE) training load and the impact that playing experience, playing position and 2-km time-trial performance had on that relationship. Microtechnology devices provided external training load (distance, mean speed, high-speed running distance, Player loadTM (PL), and Player load slowTM (PLslow)). The external training load measures had moderate to very large associations with s-RPE training load. When controlling for external training load, the 4- to 5-year players had a small increase in s-RPE training load compared to the 0- to 1- and 2- to 3-year players. Furthermore, ruckmen had moderately higher s-RPE training load than midfielders, and there was a 0.2% increase in s-RPE training load per 1 s increase in time-trial time. The aim of study 2 was to profile weekly wellness within the context of the competitive season of professional AF. Each morning before any physical training, players completed a 5-item customised self-report questionnaire (sleep quality, fatigue, stress, mood, and muscle soreness), with the mean of the individual indices used to determine overall wellness. Internal match load (s-RPE), match-to-match micro-cycle length, stage of the season and internal training load were included in multivariate linear models in order to determine their effect on weekly wellness profile. There was a lower weekly training load on a 6-day micro-cycle (mean ± s = 1813 ± 291 au) compared to a 7- (1898 ± 327 au, likely small) and 8-day (1900 ± 271 au, likely small) micro-cycle. Match load had no significant impact on weekly wellness profile, whilst there was an interaction between micro-cycle and days-post-match. There was likely to be a moderate decrease in wellness Z-score 1 d post match for an 8-day micro-cycle compared to a 6- and 7- day cycle. There was possibly a small reduction in overall wellness Z-score in the second half of the season compared to the first half of the season. Finally, training load had no effect on wellness Z-score when controlled for days-post-match, micro-cycle and stage of the season. The aim of study 3 was to assess the application of athlete self-report measures to prompt modifications to training dose by exploring its association with subsequent activity profiles. The impact of perceived wellness on a range of external load parameters, RPE and external load: RPE ratios, was explored during skill-based training in AF. Mixed-effect linear models revealed significant effects of wellness Z-score on PL and PLslow. A negative wellness Z-score corresponded to a small reduction in PL and a moderate reduction PLslow, compared to those without reduced wellness. A small reduction was also observed in the PLslow: RPE ratio models, while a small increase was seen in mean speed: RPE ratio. The aim of study 4 was to corroborate the use of particular contemporary monitoring measures by examining their effect on individual match performances. The effects of internal load parameters, combined with athlete self-reported wellness, on subjective and objective measures of match performance in 20 rounds of professional AF was examined. Acute weekly internal load (s-RPE) was determined for each independent training modality. Chronic load was calculated as the rolling 4-week mean and a training-stress balance (TSB) was ascertained by dividing the acute load (1-weekly total) by the chronic load (4-week mean) expressed as a percentage. Load from every training modality was used to calculate an overall acute load, overall chronic load, and overall TSB and only outdoor skills and conditioning sessions were used to calculate a field-based acute load, a field-based chronic load and field-based TSB. Weekly wellness was quantified as the mean of the overall daily wellness scores. An iterative linear mixed modelling approach demonstrated that load and wellness variables had minimal impact on subjective performance ratings (coaches’ votes). Conversely, objective performance, measured via Champion Data© ranking points was positively associated with load, although the magnitude of this effect was greater for field-based loads compared to overall loads. Furthermore, athletes with high loads reporting low wellness, ranked better in objective performance than those reporting high wellness with high loads. Alternatively, an increase in wellness was associated with better objective performance when accompanying lower loads. This collection of studies suggests that s-RPE has a strong relationship with measures of external load, which is moderated by playing position, experience and time-trial performance in AF and that coaches and sport scientists should give consideration to these mediators of s- RPE. It was also revealed that the weekly profile of self-reported wellness in response to matches was influenced by the match-to-match micro-cycle and stage of the season in AF. However, when factoring in these conditions, training load had minimal influence on wellness profile. As such, determination of ‘red flags’ in self-reported measures should be made against comparative weeks. Furthermore, pre-training self-reported wellness was shown to be associated with accelerometer-derived external load measures, suggesting an altered movement pattern during diminished training states. Understanding the changes in external load that might be produced, relative to the pre-training self-reported wellness, provides coaches with an opportunity to adjust prescription if warranted. Finally, the use of internal load and athlete self-report measures can be corroborated based on their relationship with an objective measure of performance in AF and the importance of a mixed-method approach to comprehensively assess athlete status is emphasised