Time to embrace the complexity when analysing GPS data? A systematic review of contextual factors on match running in rugby league

Dalton-Barron, Nicholas; Whitehead, Sarah; Roe, Gregory; Cummins, Cloe; Beggs, Clive; Jones, Ben

doi:10.1080/02640414.2020.1745446

Cited by 39 publications

(32 citation statements)

References 94 publications

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“…of the picture, its modifiability makes it a desirable target for adjustment. Many of these are specific to match circumstances [27,28] and are externally controlled (for example, venue and turnaround between games). Several refer to individual player characteristics and, therefore, depend on the practitioner's knowledge of each player to inform the decision-making process.…”

Section: Contextualising the Data In Your Environmentmentioning

confidence: 99%

More than a Metric: How Training Load is Used in Elite Sport for Athlete Management

et al. 2020

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Training load monitoring is a core aspect of modern-day sport science practice. Collecting, cleaning, analysing, interpreting, and disseminating load data is usually undertaken with a view to improve player performance and/or manage injury risk. To target these outcomes, practitioners attempt to optimise load at different stages throughout the training process, like adjusting individual sessions, planning day-to-day, periodising the season, and managing athletes with a long-term view. With greater investment in training load monitoring comes greater expectations, as stakeholders count on practitioners to transform data into informed, meaningful decisions. In this editorial we highlight how training load monitoring has many potential applications and cannot be simply reduced to one metric and/or calculation. With experience across a variety of sporting backgrounds, this editorial details the challenges and contextual factors that must be considered when interpreting such data. It further demonstrates the need for those working with athletes to develop strong communication channels with all stakeholders in the decision-making process. Importantly, this editorial highlights the complexity associated with using training load for managing injury risk and explores the potential for framing training load with a performance and training progression mindset.

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Section: Contextualising the Data In Your Environmentmentioning

confidence: 99%

More than a Metric: How Training Load is Used in Elite Sport for Athlete Management

et al. 2020

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“…Further, our model aligns with that of Vanrenterghem et al (2017) by conceptualising internal load as incorporating both physiological and biomechanical components (and subcomponents), and the work of Enoka and Duchateau (2016) by integrating perceptual and performance components of fatigue. As described, our suggested model is not intended in any way to supplant those conceptual approaches, but to offer an integrated example which has been contextualised to the specific characteristics of rugby outlined here, and elsewhere (Johnston et al, 2014a;Weaving et al, 2019b;Dalton-Barron et al, 2020;Till et al, 2020).…”

Section: Conceptual Model Of Fatiguementioning

confidence: 99%

“…Rugby players typically complete repeated rapid change of direction movements, accelerations and decelerations, sprinting, and tackling during match-play (Waldron et al, 2011;Cummins et al, 2013;Oxendale et al, 2016;Till et al, 2020). Further analysis has revealed that the demands of professional match-play vary by competition (Twist et al, 2014), and differ depending on contextual factors such as technical and tactical factors, individual characteristics, and athlete positions (Oxendale et al, 2016;Dalton-Barron et al, 2020;Till et al, 2020). The actions which contribute to external load differ between positional groups.…”

Section: Introductionmentioning

confidence: 99%

Quantifying Fatigue in the Rugby Codes: The Interplay Between Collision Characteristics and Neuromuscular Performance, Biochemical Measures, and Self-Reported Assessments of Fatigue

et al. 2021

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Locomotor and collision actions that rugby players complete during match-play often lead to substantial fatigue, and in turn, delays in recovery. The methods used to quantify post-match fatigue and recovery can be categorised as subjective and objective, with match-related collision characteristics thought to have a primary role in modulating these recovery measures. The aim of this review was to (1) evaluate how post-match recovery has been quantified in the rugby football codes (i.e., rugby league, rugby union, and rugby sevens), (2) to explore the time-course of commonly used measures of fatigue post-match, and (3) to investigate the relationships between game-related collisions and fatigue metrics. The available evidence suggests that upper-, and lower-body neuromuscular performance are negatively affected, and biomarkers of muscular damage and inflammation increase in the hours and days following match-play, with the largest differences being at 12–36 h post-match. The magnitude of such responses varies within and between neuromuscular performance (Δ ≤ 36%, n = 13 studies) and tissue biomarker (Δ ≤ 585%, n = 18 studies) measures, but nevertheless appears strongly related to collision frequency and intensity. Likewise, the increase in perceived soreness in the hours and days post-match strongly correlate to collision characteristics across the rugby football codes. Within these findings, there are specific differences in positional groups and recovery trajectories between the codes which relate to athlete characteristics, and/or locomotor and collision characteristics. Finally, based on these findings, we offer a conceptual model of fatigue which details the multidimensional latent structure of the load to fatigue relationship contextualised to rugby. Research to date has been limited to univariate associations to explore relationships between collision characteristics and recovery, and multivariate methods are necessary and recommended to account for the latent structures of match-play external load and post-match fatigue constructs. Practitioners should be aware of the typical time windows of fatigue recovery and utilise both subjective and objective metrics to holistically quantify post-match recovery in rugby.

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“…External load is typically reported as volumes (e.g., total distance) or as discretised threshold values (e.g., the distance covered at different speed thresholds) (Bourdon et al, 2017). These variables are often arbitrarily chosen (e.g., speed thresholds), and only provide a global aggregation of a series of physical actions (Dalton-Barron et al, 2020). While still important, these findings provide little context of how a player accumulated a given external load.…”

Section: Introductionmentioning

confidence: 99%

Sequential movement pattern-mining (SMP) in field-based team-sport: A framework for quantifying spatiotemporal data and improve training specificity?

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et al. 2021

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Time to embrace the complexity when analysing GPS data? A systematic review of contextual factors on match running in rugby league

Cited by 39 publications

References 94 publications

More than a Metric: How Training Load is Used in Elite Sport for Athlete Management

More than a Metric: How Training Load is Used in Elite Sport for Athlete Management

Quantifying Fatigue in the Rugby Codes: The Interplay Between Collision Characteristics and Neuromuscular Performance, Biochemical Measures, and Self-Reported Assessments of Fatigue

Sequential movement pattern-mining (SMP) in field-based team-sport: A framework for quantifying spatiotemporal data and improve training specificity?

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