Impact of Microcycle Structures on Physical and Technical Outcomes During Professional Rugby League Training and Matches

in International Journal of Sports Physiology and Performance

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Tahleya Eggers
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Rebecca Cross
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Dean Norris
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Lachlan Wilmot
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Ric Lovell
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Purpose: To assess the impact of microcycle (MC) structures on physical and technical performances in rugby league training and matches. Methods: Thirty-four professional rugby league players were monitored during all training sessions and matches across a single season wherein 2 different competition-phase MC structures were implemented. The first MC structure involved the first session on match day (MD) + 2 and the main stimulus delivered MD − 3, and the second structure delayed all sessions by 1 day (first session on MD + 3 and main session MD − 2; MC structure in the second half of the season). Physical output was quantified via relative total speed (in meters per minute), high-speed running (per minute; ≥4.0 m·s−1), and very-high-speed running (per minute; ≥5.5 m·s−1), measured using a global positioning system (10 Hz) in addition to accelerometer (100 Hz) metrics (PlayerLoad per minute and PlayerLoadslow per minute]) during training and matches. Technical performance (number of runs, meters gained, tackles made and missed) was recorded during matches. Generalized linear mixed models and equivalence tests were used to identify the impact of MC structure on physical and technical output. Results: Nonequivalent increases in meters per minute, high-speed running per minute, and PlayerLoad per minute were observed for the first training stimulus in MC structure in the second half of the season with no practical difference in midcycle sessions observed. The MC structure in the second half of the season structure resulted in increased high-speed running per minute and decreased PlayerLoadslow per minute during MD with no differences observed in technical performance. Conclusions: Delaying the first training stimulus of the MC allowed for greater training load accumulation without negative consequences in selected match running and technical performance measures. This increased MC load may support the maintenance of physical capacities across the in-season.

Rugby league is an intermittent collision-based sport, involving repeated high-intensity locomotor and tackle efforts1 that require high well-developed strength, power, and aerobic capacity.2 Throughout the Australian National Rugby League in-season, athletes typically compete in up to 24 matches across 6 months, each separated by 5 to 10 days.3 This density of matches poses unique challenges in acutely managing training stress and recovery within each microcycle,4 considering the short turnaround time and the persistent nature of muscle damage markers and compromised muscle function.1 It is commonly observed that conditioning-specific training volume is reduced in-season, and focus shifts to the technical and tactical aspects of match play.5 The efficacy of such microcycle prescription in team sports is equivocal and difficult to systematically evaluate. However, decrements in physical capacity have been observed over the course of the season,5 which may be due to compromised training stimuli owing to residual match-related fatigue. Yet, there remains a paucity of information on the impact of training schedule within the in-season microcycle on training and match performance.

According to the available case study descriptions of rugby league microcycles beyond the commonly administered taper or recovery periods prescribed around the match, there is limited consistency regarding the scheduling of training stimuli.69 Some previous case studies have reported prescribing field-based training approximately 2 days after the match day (MD + 2; approximately 48 h).1,7,9 A recent survey suggested that rugby league teams often dedicate MD + 2 to continued recovery, delivering the first training stimulus of a 7-day microcycle on MD + 3,10 a programming approach that may reflect the 48- to 96-hour postmatch recovery time course reported for neuromuscular function, muscle damage, and perceptual measures of soreness/fatigue.1 Intuitively, and theoretically, a longer recovery period may improve the capacity to train during the first session of the microcycle, but to our knowledge, this has yet to be quantified empirically.

The consequences of a delayed start to the microcycle include the potential congestion of training and/or compromising the prematch taper/preparation period. Midmicrocycle training congestion is common in contact team sports,5,8 yet the neuromuscular recovery time course following team sports field11 or resistance training12 may persist for 24 to 48 hours, which may compromise the quality of training stimuli delivered on consecutive days. Moreover, the delivery of concurrent (field and gym) sessions is common,10 which may further exacerbate the recovery time course.13 This residual fatigue and/or muscle damage may worsen perceptions of wellness and muscle soreness, reduce external load output, and impair the athlete’s capacity to perform high-intensity movements in subsequent training sessions and matches.13,14 However, technical performance is considered more relevant for successful match outcomes,15 with residual fatigue having the potential to negatively impact technical skills.13 As a result, the previously reported scheduling of training sessions on MD − 2 (48 h before a match)1,7,9 may be questioned considering the potential for residual fatigue to impair both physical output and technical performance during subsequent match play.

Although physical performance in matches could be subtly compromised when concurrent training sessions are scheduled on MD − 2, technical performance outcomes (ie, tackle outcomes, meters gained) are considered more relevant for successful performance outcomes.1517 The influence of fatigue on technical performance in team sports has primarily focused on the relationship between acute match-induced fatigue, signified by concurrent declines in both physical output and technical performance.18 To our knowledge, the impact of different microcycle structures on technical match performance indicators has not been examined, which may be relevant considering that preexisting (residual) fatigue has been suggested to impair subsequent physical performance.14

The overall lack of consensus on microcycle scheduling suggests that more research is warranted to gain a better understanding concerning the timing of training stimuli relative to match play. Therefore, the current study aims to identify the impact of differing rugby league microcycle structures on (1) the physical loads within on-field training sessions and (2) the physical and technical performances during competitive matches.

Methods

Subjects

Thirty-four professional male rugby league athletes (age 26 [4] y, height 185.9 [5.6] cm, body mass 100.6 [9.7] kg; 1.2-km time trial: 4:57 [0:15] min: s, peak speed [global positioning system]: 9.1 [0.5] m·s−1; one-repetition maximum bench press: 137 [15] kg; one-repetition maximum squat: 180 [24] kg; percentage of body fat: 14.0% [2.4%]), from a National Rugby League club, participated in this study. Informed consent and university human ethics approval (Western Sydney University: H13663) was gained before the commencement of this research.

Design

A longitudinal observation was conducted across a single National Rugby League season (26 wk; March–October). The physical and technical outcomes of each participant were measured across 2 different microcycle structures. Physical outcomes were measured during all field-based training sessions and matches, and technical outcomes were measured in matches only. Each respective microcycle involved 1 to 3 field-based training sessions, a low-intensity tactical session (Tprep), one match, and a recovery session (massage, compression, cold-water immersion, and mobility) on the day following the match. The first session(s) of the microcycle (Tskills) primarily focused on football skills training, including some conditioning drills, whereas the last training session completed across the microcycle (Tmain) primarily involved technical/tactical preparation for the subsequent match. Across the first half of the season (MCearly), Tskills was completed on match day (MD) +2 (38–42 h following match play). The Tmain session was delivered on MD − 3, allowing 2 days of tapering before the match, with Tprep completed on MD − 1. Tmain and Tskills were completed in the morning with gym-based resistance and contact/wrestle sessions performed in the afternoon following a short recovery period. The microcycle structure in the second half of the season (MCdelay) was modified to provide an additional postmatch recovery day with Tskills on MD + 3 and Tmain delivered closer to the match (MD − 2). The Tprep session was consistently delivered on MD − 1 in both MCearly and MCdelay, but physical output was not monitored due to their taper focus. In addition, the arrangement of Tmain sessions was modified to schedule resistance-based training before the field component of the sessions (Figure 1).

Figure 1
Figure 1

—MCearly versus MCdelay schedule schematic including the timing of training sessions per day in relation to the upcoming match during 7- and 9-day microcycles. MD indicates match day; MCdelay, microcycle structure in the second half of the season; MCearly, microcycle structure in the first half of the season. Positive (+) denotes relation to previous match, and negative (−) denotes relation to forthcoming match.

Citation: International Journal of Sports Physiology and Performance 17, 5; 10.1123/ijspp.2021-0307

For analysis, microcycles comprising shorter (5–6 d) turnarounds were excluded as they did not involve both Tskills and Tmain sessions. Eight-day microcycles were excluded due to limited occurrences (n = 2) and because they varied from the other microcycle scheduling structures. Accordingly, MCearly comprised 5 microcycles and 458 individual observations (Tskills: 154, Tmain: 134, and match: 79) available for analysis, with 7 and 557 (Tskills: 185, Tmain: 189, and match: 116) available for MCdelay, respectively.

Methodology

The physical output of each athlete was measured using 10 Hz global positioning systems (OptimEye S5; Catapult Sports, Melbourne, Australia) positioned in a between-scapulae position, housed in custom-designed garments during training sessions, and within a pocket within their match jerseys (tailored to the size of the unit). Each participant wore the same device for all training sessions and wore the same sized garment and jersey to reduce any intraunit variability in data associated with movement artifact.19 The same unit was worn in match play except for matches completed at stadiums equipped with 20-Hz local positioning system technology (n = 8; MCearly: 4 and MCdelay: 4) where alternative units (ClearSky T6; Catapult Sports) were used, as is standard industry practice. The 10-Hz global positioning system, 20-Hz local positioning system, and 100-Hz accelerometer-derived metrics have been previously reported to have acceptable levels of measurement accuracy during team sport activity.20,21

Data were downloaded using the manufacturer’s software (Catapult OpenField—version 1.18.1; Catapult Sports) with the associated “Intelligent Motion Filter” enabled with a dwell time of 0.4 seconds. Data exclusion criteria were based on signal quality using horizontal dilution of precision (≥2.01) and the number of connected satellites (≤6).20 Session average of these parameters for all observations was satisfactory and, therefore, available for analysis. Total training and match exposure were calculated as the total duration of each session. Warm-ups were included, but rest periods between drills were trimmed before analysis. Locomotor measures included total distance covered, high-speed running (HSR·min−1; ≥4.0 m·s−1),22 and very high-speed running (≥5.5 m·s−1)11 distance. Accelerometer-based metrics (ie, PlayerLoad; PL) were also included as they quantified high-intensity movements occurring at low speed (eg, accelerations, decelerations, change of direction, collision), which are inherent to rugby league.23,24 In particular, PLslow (total PL accumulated at velocities below 2 m·s−1) captured these high-energetic activities occurring at low speed, in particular wrestle components of collision-based activities.25 All locomotor displacement and accelerometer metrics were analyzed per session and per microcycle, expressed per minute of exposure (in meters per minute [m·min−1] and PL per minute [PL·min−1]).

Technical statistics from each match, including meters gained (with the ball), frequency of runs with the ball, tackles made (successful tackle completion), and tackles missed (incomplete tackle efforts) were quantified for each player by the teams’ performance analyst via match footage (Rugby League Analyzer; Fair Play Pty Ltd, Jindalee, QLD, Australia). These measures have been demonstrated to distinguish between successful and less successful Rugby League teams in previous research.1517

Statistical Analysis

Due to the fixed (convenience) sample size within the study design, a sensitivity analysis was run a priori to identify the smallest effect size of interest that could be detected with 80% power with an alpha level of .05. This was achieved by selecting a 2-tailed “paired mean difference” option in G*Power and specifying the aforementioned power and alpha levels for a sample size of 34.

Due to the varying nature in outcome distributions expected between physical and technical measures assessed, a series of generalized linear mixed-effects models (lme4, glmer, and glmmtmb)26,27 were fit all within R Studio (R Foundation for Statistical Computing, Vienna, Austria). Physical outcome-based variables including meters gained and tackles made were fit assuming a Gaussian distribution, whereas runs and missed tackles were fit with a Poisson and zero-inflated negative binomial distribution, respectively. Examination of residual plots were performed using both performance package28 for linear models and dharma package29 for Poisson and zero-inflated models. An interaction term between microcycle phase and training type was included as a fixed effect to assess the potential impact on external load variables. Modeling of technical outcomes included microcycle type and a covariate of minutes played as fixed effects to account for differences in minutes played over the season. Both model types included a random intercept term for individuals to account for the repeated-measures design. Equivalence testing was then used to identify whether we could reject the hypothesis of equivalence between estimated marginal means for microcycle type given our identified smallest effect size of interest from sensitivity analysis (Cohen dz = ±0.44) and an alpha level of <.05. Differences between estimated marginal means were reported with 95% confidence intervals (CIs) with P values reported representing the significance for equivalence.

Results

Physical Outcomes

While statistically significant from null variables were observed for a range of variables, the magnitude of effects was considered practically equivalent when considering the smallest effect size of interest region. Failure to reject the absence of a practically meaningful difference between microcycle conditions was observed for m·min−1 during Tskills session (est: 12.1; 95% CI, 10.2 to 13.9; P ≥ .99), HSR·min−1 in both Tskills and matches (Tskills est: 3.6; 95% CI, 2.9 to 4.5; P ≥ .99 and matches est: 2.1; 95% CI, 1.4 to 2.9, P = .82), very high-speed running per minute for matches (est: 0.85; 95% CI, 0.39 to 1.33; P = .20), and PL·min−1 in Tskills session (est: 1.59; 95% CI, 1.37 to 1.81; P ≥ .99) with all measures increased within MCdelay. Conversely, PLslow·min−1 was lower in matches contested in MCearly (est: −0.8; 95% CI, −0.92 to −0.69; P ≥ .99).

Technical Outcomes

The influence of microcycle on technical outcomes was estimated as the mean difference between matches played at an average length (74 min). No statistically significant differences from the null or practically meaningful differences were observed for any technical outcomes between microcycle conditions.

Discussion

The current study aimed to determine the impact of 2 microcycle structures on physical output in field-based training sessions and both physical and technical output during match play in professional rugby league. From a training perspective, affording an extra day of recovery at the start of the microcycle (Tskills from MD + 2 to MD + 3) resulted in an increased training load within the Tskills session, whereas the physical output was maintained when the Tmain session was delayed to MD − 2. There was an increase HSR·min−1 and a decrease in PLslow in matches during MCdelay versus MCearly, but there was no impact on technical performance metrics.

Despite alterations in on-field training schedules, there were no differences in the duration of training sessions or match minutes played between the 2 microcycles. The most notable differences between microcycle structures occurred during the first training stimulus (Tskills). Increases in m·min−1, HSR·min−1, and PL·min−1 were observed when 2 full days postmatch recovery was provided (Figure 2). Previous research has demonstrated that decrements in neuromuscular function and muscle soreness persist for up to 48 hours postmatch1; therefore, the extended time between match play and training afforded in MCdelay may have been sufficient in ensuring complete recovery and facilitating greater capacity to perform high-intensity activity. Although not measured in this study, previous research in Australian football has reported that worsened ratings of subjective fatigue, soreness, or compromised wellness are associated with reductions in the external load of subsequent training sessions,14 which may have contributed to the comparatively lower physical output when limited postmatch recovery was afforded in MCearly. Given that practitioners within collision-based sports have recognized the physical cost of previous match and athlete recovery status as the most influential factors of subsequent training prescription,10 deliberate changes in prescription by coaches/support staff may have contributed to the variation in Tskills sessions between microcycle structures.

Figure 2
Figure 2

—Estimated marginal means and SDs for MCearly and MCdelay in training and matches. Shaded areas represent the smallest effect size of interest converted from the standardized scale to absolute units. HSR indicates high-speed running; MCdelay, microcycle structure in the second half of the season; MCearly, microcycle structure in the first half of the season; PL, PlayerLoad; VHSR, very high-speed running. aContrast between estimated marginal means, which failed to reject the absence of a possible meaningful effect given our predefined smallest effect size of interest.

Citation: International Journal of Sports Physiology and Performance 17, 5; 10.1123/ijspp.2021-0307

A recent study surveying current approaches to in-season training prescription suggests that the first training stimulus of the microcycle across rugby league teams most commonly occurs on MD + 3,10 in alignment with the scheduling approach observed in MCdelay. The consequence of delaying the first training stimulus is midcycle training congestion with a view to preserving the late-cycle taper leading into match play.5,8 An approach to relieve this congestion is to provide a further recovery day between training sessions to minimize an exacerbated fatigue response associated with back-to-back sessions,13 delaying midcycle stimulus to within 2 days of the subsequent match (MD − 2).1,7,9 For this case study, the physical output achieved throughout Tmain sessions, regardless of scheduling on MD − 3 or MD − 2, was considered practically equivalent (Figure 2). The combination of increased load accumulated from the delayed first stimulus (Tskills on MD + 3) with equivalent output in the Tmain sessions (MD − 2) suggests that the MCdelay structure resulted in greater total microcycle load (m·min−1, HSR·min−1, and PL·min−1). Given that decrements in physical capacity (particularly across measures of power5,30 and aerobic capacity30) throughout the in-season period and recovery implications of sessions scheduled back-to-back11 have previously been identified as a concern, this approach may afford greater opportunity for load accumulation, without compromising recovery strategies, across medium- to long-term turnarounds (7-9 or more days, respectively). The impact of microcycle structure upon resistance and power-based training outcomes was not measured in this study, and further research is warranted in this area to further inform scheduling decisions.

Understanding that neuromuscular function may be impaired for up to 48 hours after team sports training,11,12 the implementation of a training stimulus within 2 days of the upcoming match (MD − 2)1,7,9 could theoretically impair match physical output. However, the results of this study found that delaying the delivery of the Tmain stimuli to MD − 2 had no adverse outcomes on match physical output. Practically meaningful increases in HSR·min−1 were observed in matches across MCdelay, whereas there was a significant decrease in PLslow·min−1. The reasons for these observed differences are unclear, although it is expected that the composition of match scenarios, rather than match outcome, may be more influential considering the equivalent proportion of wins and losses in matches during MCearly and MCdelay. A change in the composition of match play (attack vs defensive periods) may explain the changes observed in the current study, with the increase in HSR·min−1 and lower PLslow indicative of less tackle/wrestle activities in MCdelay matches. However, since the number of tackle involvements did not differ between microcycle conditions, locomotor differences may be more indicative of wrestle-based involvements that were not captured in the technical indices adopted herein. Alternatively, lower HSR·min−1 has been observed in matches contested during the early stages of the season,22 which may contribute to the increase we observed in MCdelay matches. With the increased training load accumulation across MCdelay, a potential increase in physical capacity may support a higher physical output in match play, considering that increased physical fitness is positively associated with HSR distance.22 Despite the changes in match physical outcomes identified here, the ability to execute technical skills is considered more critical to successful match outcomes.15

The complex influence of contextual match factors can impact the variation of both physical and technical performance.31 In particular, there is a greater between-match variability in technical versus physical outcome parameters.31 This intermatch variability coupled with the modest sample size in our study may explain the large confidence intervals observed in the technical indices quantified in the current study. Although differences in physical output throughout the microcycle were observed according to the scheduling approach (Figure 2), when the main training stimulus was delivered closer to match play, there was not a statistically or practically meaningful impact upon match technical performance. This implies that delivering the main training closer to the match has no residual consequences on technical components of match performance. Nonetheless, it is likely that situational factors such as match status, outcome, and tactical approaches also influence these performance indices.1517

To our knowledge, this study was the first to analyze the direct impact of altering within-microcycle schedules on training and match output. Although this study provides a novel insight into this area, we acknowledge that there are limitations that should be considered. This case study reflects a single club’s training regimen, and therefore, a multicenter approach may be warranted considering the broad range of contextual factors that could impact the outcome measures examined (ie, match status/tactics, kickoff time, travel schedule, environmental conditions). The quantification of all forms of training stimuli incurred throughout the microcycles (resistance and contact/wrestle) may allow for a more holistic understanding of the impact of scheduling changes, concerning weekly and daily scheduling choices. In addition, the current study was focused on changes in outputs across the microcycle, and measurement of performance capacity (ie, muscle function), underlying mechanisms (ie, muscle damage, fatigue origins), and perceptual responses (ie, ratings of perceived exertion/fatigue, wellness constructs) in future research would provide further context to the changes observed. Finally, data pertaining to the measurement properties of the technical metrics used in this study were unavailable yet are widely adopted in both previous research16,17 and industry practice.

Practical Applications

  1. 1.Delaying the first training session of the microcycle to MD + 3 may allow for the opportunity to deliver greater training load, leading to greater load accumulation across the whole microcycle.
  2. 2.Increased total training load incurred from the delayed start to the microcycle may support the maintenance of physical capacities across the in-season.
  3. 3.Delaying the midcycle main training stimulus may reduce midweek training congestion and the need to schedule back-to-back on-field training days across medium- to long-term turnarounds (7 and 9+ d, respectively).

Conclusions

The study investigated the impact of varying in-season microcycle structures on physical output of professional rugby league players in training and matches. In addition, subsequent impact on technical match outcomes was analyzed. Greater distance (m·min−1), HSR (HSR·min−1), and total PL (PL·min−1) was observed when the first on-field training session was completed on MD + 3 versus MD + 2. Completing Tmain sessions on MD − 2 as opposed to MD − 3 did not change physical outcomes of the field-based session. The structure of MCdelay and resultant physical output did not have any negative impact on physical or technical performances in subsequent matches.

Acknowledgments

The authors would like to thank the Parramatta Eels National Rugby League players and staff for their participation in this study.

References

  • 1.

    McLean BD, Coutts AJ, Kelly V, McGuigan MR, Cormack SJ. Neuromuscular, endocrine, and perceptual fatigue responses during different length between-match microcycles in professional rugby league players. Int J Sports Physiol Perform. 2010;5(3):367383. PubMed ID: 20861526 doi:10.1123/ijspp.5.3.367

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 2.

    Gabbett TJ, King T, Jenkins D. Applied physiology of rugby league. Sports Med. 2008;38(2):119138. PubMed ID: 18201115 doi:10.2165/00007256-200838020-00003

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 3.

    Murray NB, Gabbett TJ, Chamari K. Effect of different between-match recovery times on the activity profiles and injury rates of national rugby league players. J Strength Cond Res. 2014;28(12):34763483. PubMed ID: 24983851 doi:10.1519/JSC.0000000000000603

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 4.

    Harries SK, Lubans DR, Callister R. Systematic review and meta-analysis of linear and undulating periodized resistance training programs on muscular strength. J Strength Cond Res. 2015;29(4):11131125. PubMed ID: 25268290 doi:10.1519/JSC.0000000000000712

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 5.

    Argus CK, Gill ND, Keogh JW, Hopkins WG, Beaven CM. Changes in strength, power, and steroid hormones during a professional rugby union competition. J Strength Cond Res. 2009;23(5):15831592. PubMed ID: 19620903 doi:10.1519/JSC.0b013e3181a392d9

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 6.

    Moreira A, Kempton T, Aoki MS, Sirotic AC, Coutts AJ. The impact of 3 different-length between-matches microcycles on training loads in professional rugby league players. Int J Sports Physiol Perform. 2015;10(6):767773. PubMed ID: 26023860 doi:10.1123/ijspp.2015-0100

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 7.

    McLellan CP, Lovell DI, Gass GC. Creatine kinase and endocrine responses of elite players pre, during, and post rugby league match play. J Strength Cond Res. 2010;24(11):29082919. PubMed ID: 20703171 doi:10.1519/JSC.0b013e3181c1fcb1

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 8.

    Baker D. The effects of an in-season of concurrent training on the maintenance of maximal strength and power in professional and collegeaged rugby league football players. J Strength Cond Res. 2001;25(2):172177.

    • Search Google Scholar
    • Export Citation
  • 9.

    Morehen JC, Bradley WJ, Clarke J, et al. The assessment of total energy expenditure during a 14-day in-season period of professional rugby league players using the doubly labelled water method. Int J Sport Nutr Exerc Metab. 2016;26(5):464472. PubMed ID: 27096279 doi:10.1123/ijsnem.2015-0335

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 10.

    Cross R, Siegler J, Marshall P, Lovell R. Scheduling of training and recovery during the in-season weekly micro-cycle: insights from team sport practitioners. Eur J Sport Sci. 2019;19(10):12871296. PubMed ID: 30922202 doi:10.1080/17461391.2019.1595740

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 11.

    Johnston RD, Gabbett TJ, Seibold AJ, Jenkins DG. Influence of physical contact on neuromuscular fatigue and markers of muscle damage following small-sided games. J Sci Med Sport. 2014;17(5):535540. PubMed ID: 23981503 doi:10.1016/j.jsams.2013.07.018

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 12.

    Marshall PWM, Cross R, Haynes M. The fatigue of a full body resistance exercise session in trained men. J Sci Med Sport. 2018;21(4):422426. PubMed ID: 28716692 doi:10.1016/j.jsams.2017.06.020

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 13.

    Sparkes W, Turner AN, Cook CJ, et al. The neuromuscular, endocrine and mood responses to a single versus double training session day in soccer players. J Sci Med Sport. 2020;23(1):6974. PubMed ID: 31526664 doi:10.1016/j.jsams.2019.08.291

    • Search Google Scholar
    • Export Citation
  • 14.

    Gallo TF, Cormack SJ, Gabbett TJ, Lorenzen CH. Pre-training perceived wellness impacts training output in Australian football players. J Sports Sci. 2016;34(15):14451451. PubMed ID: 26637525 doi:10.1080/02640414.2015.1119295

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 15.

    Gabbett TJ. Effects of physical, technical, and tactical factors on final ladder position in semiprofessional rugby league. Int J Sports Physiol Perform. 2014;9(4):680688. PubMed ID: 24231062 doi:10.1123/ijspp.2013-0253

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 16.

    Woods CT, Sinclair W, Robertson S. Explaining match outcome and ladder position in the National Rugby League using team performance indicators. J Sci Med Sport. 2017;20(12):11071111. PubMed ID: 28479281 doi:10.1016/j.jsams.2017.04.005

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 17.

    Kempton T, Sirotic AC, Coutts AJ. A comparison of physical and technical performance profiles between successful and less-successful professional rugby league teams. Int J Sports Physiol Perform. 2017;12(4):520526. PubMed ID: 27617478 doi:10.1123/ijspp.2016-0003

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 18.

    Kempton T, Sirotic AC, Cameron M, Coutts AJ. Match-related fatigue reduces physical and technical performance during elite rugby league match-play: a case study. J Sports Sci. 2013;31(16):17701780. PubMed ID: 23751106 doi:10.1080/02640414.2013.803583

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 19.

    McLean BD, Cummins C, Conlan G, Duthie G, Coutts AJ. The fit matters: influence of accelerometer fitting and training drill demands on load measures in rugby league players. Int J Sports Physiol Perform. 2018;13(8):10831089. PubMed ID: 29431556 doi:10.1123/ijspp.2017-0729

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 20.

    Malone JJ, Lovell R, Varley MC, Coutts AJ. Unpacking the black box: applications and considerations for using GPS devices in sport. Int J Sports Physiol Perform. 2017;12(suppl 2):S218S226. doi:10.1123/ijspp.2016-0236

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 21.

    Rico-González M, Los Arcos A, Clemente FM, Rojas-Valverde D, Pino-Ortega J. Accuracy and reliability of local positioning systems for measuring sport movement patterns in stadium-scale: a systematic review. Appl Sci. 2020;10(17):5994. doi:10.3390/app10175994

    • Search Google Scholar
    • Export Citation
  • 22.

    Kempton T, Coutts AJ. Factors affecting exercise intensity in professional rugby league match-play. J Sci Med Sport. 2016;19(6):504508. PubMed ID: 26117160 doi:10.1016/j.jsams.2015.06.008

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 23.

    Hulin BT, Gabbett TJ, Johnston RD, Jenkins DG. PlayerLoad variables: sensitive to changes in direction and not related to collision workloads in rugby league match play. Int J Sports Physiol Perform. 2018;13(9):11361142. PubMed ID: 29543076 doi:10.1123/ijspp.2017-0557

    • Search Google Scholar
    • Export Citation
  • 24.

    Gabbett TJ. Relationship between accelerometer load, collisions, and repeated high-intensity effort activity in rugby league players. J Strength Cond Res. 2015;29(12):3424. PubMed ID: 26196661 doi:10.1519/JSC.0000000000001017

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 25.

    Boyd LJ, Ball K, Aughey RJ. The reliability of MinimaxX accelerometers for measuring physical activity in Australian football. Int J Sports Physiol Perform. 2011;6(3):311321. PubMed ID: 21911857 doi:10.1123/ijspp.6.3.311

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 26.

    Bates D, Mächler M, Bolker BM, Walker S. Fitting linear mixed-effects models using lme4. arXiv preprint arXiv:14065823. 2014.

  • 27.

    Brooks ME, Kristensen K, van Benthem KJ, et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R J. 2017;9(2):378400. doi:10.32614/RJ-2017-066

    • Search Google Scholar
    • Export Citation
  • 28.

    Lüdecke D, Ben-Shachar M, Patil I, Waggoner P, Makowski D. Performance: an R package for assessment, comparison and testing of statistical models. J. Open Source Softw. 2021;6(60):3139. doi:10.21105/joss.03139

    • Search Google Scholar
    • Export Citation
  • 29.

    Hartig F. DHARMa: residual diagnostics for hierarchical (multi-level/mixed) regression models. R package version 01 0. CRAN/GitHub;2016.

    • Search Google Scholar
    • Export Citation
  • 30.

    Gabbett TJ. Changes in physiological and anthropometric characteristics of rugby league players during a competitive season. J Strength Cond Res. 2005;19(2):400408. PubMed ID: 15903382

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 31.

    Kempton T, Sullivan C, Bilsborough JC, Cordy J, Coutts AJ. Match-to-match variation in physical activity and technical skill measures in professional Australian Football. J Sci Med Sport. 2015;18(1):109113. PubMed ID: 24444753 doi:10.1016/j.jsams.2013.12.006

    • PubMed
    • Search Google Scholar
    • Export Citation

Eggers, Cross, Norris, and Lovell are with the School of Health Sciences, Western Sydney University, Penrith, NSW, Australia. Eggers and Wilmot are with Parramatta Eels National Rugby League, Parramatta, NSW, Australia.

Eggers (17694995@student.westernsydney.edu.au) is corresponding author.
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    Figure 1

    —MCearly versus MCdelay schedule schematic including the timing of training sessions per day in relation to the upcoming match during 7- and 9-day microcycles. MD indicates match day; MCdelay, microcycle structure in the second half of the season; MCearly, microcycle structure in the first half of the season. Positive (+) denotes relation to previous match, and negative (−) denotes relation to forthcoming match.

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    Figure 2

    —Estimated marginal means and SDs for MCearly and MCdelay in training and matches. Shaded areas represent the smallest effect size of interest converted from the standardized scale to absolute units. HSR indicates high-speed running; MCdelay, microcycle structure in the second half of the season; MCearly, microcycle structure in the first half of the season; PL, PlayerLoad; VHSR, very high-speed running. aContrast between estimated marginal means, which failed to reject the absence of a possible meaningful effect given our predefined smallest effect size of interest.

  • 1.

    McLean BD, Coutts AJ, Kelly V, McGuigan MR, Cormack SJ. Neuromuscular, endocrine, and perceptual fatigue responses during different length between-match microcycles in professional rugby league players. Int J Sports Physiol Perform. 2010;5(3):367383. PubMed ID: 20861526 doi:10.1123/ijspp.5.3.367

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 2.

    Gabbett TJ, King T, Jenkins D. Applied physiology of rugby league. Sports Med. 2008;38(2):119138. PubMed ID: 18201115 doi:10.2165/00007256-200838020-00003

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 3.

    Murray NB, Gabbett TJ, Chamari K. Effect of different between-match recovery times on the activity profiles and injury rates of national rugby league players. J Strength Cond Res. 2014;28(12):34763483. PubMed ID: 24983851 doi:10.1519/JSC.0000000000000603

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 4.

    Harries SK, Lubans DR, Callister R. Systematic review and meta-analysis of linear and undulating periodized resistance training programs on muscular strength. J Strength Cond Res. 2015;29(4):11131125. PubMed ID: 25268290 doi:10.1519/JSC.0000000000000712

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 5.

    Argus CK, Gill ND, Keogh JW, Hopkins WG, Beaven CM. Changes in strength, power, and steroid hormones during a professional rugby union competition. J Strength Cond Res. 2009;23(5):15831592. PubMed ID: 19620903 doi:10.1519/JSC.0b013e3181a392d9

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 6.

    Moreira A, Kempton T, Aoki MS, Sirotic AC, Coutts AJ. The impact of 3 different-length between-matches microcycles on training loads in professional rugby league players. Int J Sports Physiol Perform. 2015;10(6):767773. PubMed ID: 26023860 doi:10.1123/ijspp.2015-0100

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 7.

    McLellan CP, Lovell DI, Gass GC. Creatine kinase and endocrine responses of elite players pre, during, and post rugby league match play. J Strength Cond Res. 2010;24(11):29082919. PubMed ID: 20703171 doi:10.1519/JSC.0b013e3181c1fcb1

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 8.

    Baker D. The effects of an in-season of concurrent training on the maintenance of maximal strength and power in professional and collegeaged rugby league football players. J Strength Cond Res. 2001;25(2):172177.

    • Search Google Scholar
    • Export Citation
  • 9.

    Morehen JC, Bradley WJ, Clarke J, et al. The assessment of total energy expenditure during a 14-day in-season period of professional rugby league players using the doubly labelled water method. Int J Sport Nutr Exerc Metab. 2016;26(5):464472. PubMed ID: 27096279 doi:10.1123/ijsnem.2015-0335

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 10.

    Cross R, Siegler J, Marshall P, Lovell R. Scheduling of training and recovery during the in-season weekly micro-cycle: insights from team sport practitioners. Eur J Sport Sci. 2019;19(10):12871296. PubMed ID: 30922202 doi:10.1080/17461391.2019.1595740

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 11.

    Johnston RD, Gabbett TJ, Seibold AJ, Jenkins DG. Influence of physical contact on neuromuscular fatigue and markers of muscle damage following small-sided games. J Sci Med Sport. 2014;17(5):535540. PubMed ID: 23981503 doi:10.1016/j.jsams.2013.07.018

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 12.

    Marshall PWM, Cross R, Haynes M. The fatigue of a full body resistance exercise session in trained men. J Sci Med Sport. 2018;21(4):422426. PubMed ID: 28716692 doi:10.1016/j.jsams.2017.06.020

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 13.

    Sparkes W, Turner AN, Cook CJ, et al. The neuromuscular, endocrine and mood responses to a single versus double training session day in soccer players. J Sci Med Sport. 2020;23(1):6974. PubMed ID: 31526664 doi:10.1016/j.jsams.2019.08.291

    • Search Google Scholar
    • Export Citation
  • 14.

    Gallo TF, Cormack SJ, Gabbett TJ, Lorenzen CH. Pre-training perceived wellness impacts training output in Australian football players. J Sports Sci. 2016;34(15):14451451. PubMed ID: 26637525 doi:10.1080/02640414.2015.1119295

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 15.

    Gabbett TJ. Effects of physical, technical, and tactical factors on final ladder position in semiprofessional rugby league. Int J Sports Physiol Perform. 2014;9(4):680688. PubMed ID: 24231062 doi:10.1123/ijspp.2013-0253

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 16.

    Woods CT, Sinclair W, Robertson S. Explaining match outcome and ladder position in the National Rugby League using team performance indicators. J Sci Med Sport. 2017;20(12):11071111. PubMed ID: 28479281 doi:10.1016/j.jsams.2017.04.005

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 17.

    Kempton T, Sirotic AC, Coutts AJ. A comparison of physical and technical performance profiles between successful and less-successful professional rugby league teams. Int J Sports Physiol Perform. 2017;12(4):520526. PubMed ID: 27617478 doi:10.1123/ijspp.2016-0003

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 18.

    Kempton T, Sirotic AC, Cameron M, Coutts AJ. Match-related fatigue reduces physical and technical performance during elite rugby league match-play: a case study. J Sports Sci. 2013;31(16):17701780. PubMed ID: 23751106 doi:10.1080/02640414.2013.803583

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 19.

    McLean BD, Cummins C, Conlan G, Duthie G, Coutts AJ. The fit matters: influence of accelerometer fitting and training drill demands on load measures in rugby league players. Int J Sports Physiol Perform. 2018;13(8):10831089. PubMed ID: 29431556 doi:10.1123/ijspp.2017-0729

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 20.

    Malone JJ, Lovell R, Varley MC, Coutts AJ. Unpacking the black box: applications and considerations for using GPS devices in sport. Int J Sports Physiol Perform. 2017;12(suppl 2):S218S226. doi:10.1123/ijspp.2016-0236

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 21.

    Rico-González M, Los Arcos A, Clemente FM, Rojas-Valverde D, Pino-Ortega J. Accuracy and reliability of local positioning systems for measuring sport movement patterns in stadium-scale: a systematic review. Appl Sci. 2020;10(17):5994. doi:10.3390/app10175994

    • Search Google Scholar
    • Export Citation
  • 22.

    Kempton T, Coutts AJ. Factors affecting exercise intensity in professional rugby league match-play. J Sci Med Sport. 2016;19(6):504508. PubMed ID: 26117160 doi:10.1016/j.jsams.2015.06.008

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 23.

    Hulin BT, Gabbett TJ, Johnston RD, Jenkins DG. PlayerLoad variables: sensitive to changes in direction and not related to collision workloads in rugby league match play. Int J Sports Physiol Perform. 2018;13(9):11361142. PubMed ID: 29543076 doi:10.1123/ijspp.2017-0557

    • Search Google Scholar
    • Export Citation
  • 24.

    Gabbett TJ. Relationship between accelerometer load, collisions, and repeated high-intensity effort activity in rugby league players. J Strength Cond Res. 2015;29(12):3424. PubMed ID: 26196661 doi:10.1519/JSC.0000000000001017

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 25.

    Boyd LJ, Ball K, Aughey RJ. The reliability of MinimaxX accelerometers for measuring physical activity in Australian football. Int J Sports Physiol Perform. 2011;6(3):311321. PubMed ID: 21911857 doi:10.1123/ijspp.6.3.311

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 26.

    Bates D, Mächler M, Bolker BM, Walker S. Fitting linear mixed-effects models using lme4. arXiv preprint arXiv:14065823. 2014.

  • 27.

    Brooks ME, Kristensen K, van Benthem KJ, et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R J. 2017;9(2):378400. doi:10.32614/RJ-2017-066

    • Search Google Scholar
    • Export Citation
  • 28.

    Lüdecke D, Ben-Shachar M, Patil I, Waggoner P, Makowski D. Performance: an R package for assessment, comparison and testing of statistical models. J. Open Source Softw. 2021;6(60):3139. doi:10.21105/joss.03139

    • Search Google Scholar
    • Export Citation
  • 29.

    Hartig F. DHARMa: residual diagnostics for hierarchical (multi-level/mixed) regression models. R package version 01 0. CRAN/GitHub;2016.

    • Search Google Scholar
    • Export Citation
  • 30.

    Gabbett TJ. Changes in physiological and anthropometric characteristics of rugby league players during a competitive season. J Strength Cond Res. 2005;19(2):400408. PubMed ID: 15903382

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 31.

    Kempton T, Sullivan C, Bilsborough JC, Cordy J, Coutts AJ. Match-to-match variation in physical activity and technical skill measures in professional Australian Football. J Sci Med Sport. 2015;18(1):109113. PubMed ID: 24444753 doi:10.1016/j.jsams.2013.12.006

    • PubMed
    • Search Google Scholar
    • Export Citation
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