Assessing pre-season workload variation in professional rugby union players by comparing three acute:Chronic workload ratio models based on playing positions

Quantifying the pre-season workload of professional Rugby Union players, in relation to their respective positions not only provides crucial insights into their physical demands and training needs but also underscores the significance of the acute:chronic workload ratio (ACWR) in assessing workload. However, given the diversity in ACWR calculation methods, their applicability requires further exploration. As a result, this study aims to analyze the workload depending on the player's positions and to compare three ACWR calculation methods. Fifty-seven players were categorized into five groups based on their playing positions: tight five (T5), third-row (3R), number nine (N9), center, and third line defense (3L). The coupled and uncoupled rolling averages (RA), as well as the exponentially weighted moving average ACWR method, were employed to compute measures derived from GPS data. Changes throughout the pre-season were assessed using the one-way and two-way analysis of variance. The results revealed that N9 covered significantly greater distances and exhibited higher player load compared to T5 and 3L [p < 0.05, effect size (ES) = 0.16-0.68]. Additionally, 3L players displayed the highest workload across various measures, including counts of accelerations and decelerations (>2.5 m s<sup>-2</sup>), accelerations (>2.5 m s<sup>-2</sup>), acceleration distance (>2 m s<sup>-2</sup>), high-speed running (>15 km h<sup>-1</sup>), very high-speed running (>21 km h<sup>-1</sup>, VSHR), sprint running (>25 km h<sup>-1</sup>, SR) distance. When using coupled RA ACWR method, centers exposed significantly greater values to T5 (p < 0.05, ES = 0.8) and 3R (p < 0.05, ES = 0.83). Moreover, centers exhibited greater (p < 0.05, ES = 0.67-0.91) uncoupled RA ACWR values for VHSR and SR than T5 and 3R. When comparing the three ACWR methods, although significant differences emerged in some specific cases, the ES were all small (0-0.56). In light of these findings, training should be customized to the characteristics of players in different playing positions and the three ACWR calculation methods can be considered as equally effective approaches.

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Publication Details

Journal
Heliyon
Published
2024-08-30
DOI
https://doi.org/10.1016/j.heliyon.2024.e37176
Citations
4
Primary Topic
Sports Performance and Training
Type
article
Field-Weighted Citation Impact
1.91
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article

Assessing pre-season workload variation in professional rugby union players by comparing three acute:Chronic workload ratio models based on playing positions

Jacques Prioux, Shuzhe Ding, Kilian Philippe, Xiaopan Hu et al.
4 citations
Heliyon
Sports Performance and Training
1.91
article

Assessing pre-season workload variation in professional rugby union players by comparing three acute:Chronic workload ratio models based on playing positions

Jacques Prioux, Shuzhe Ding, Kilian Philippe, Xiaopan Hu, Simon Boisbluche, Mathieu Demy, Xiangyu Ren
article en
4 citations

Abstract

Quantifying the pre-season workload of professional Rugby Union players, in relation to their respective positions not only provides crucial insights into their physical demands and training needs but also underscores the significance of the acute:chronic workload ratio (ACWR) in assessing workload. However, given the diversity in ACWR calculation methods, their applicability requires further exploration. As a result, this study aims to analyze the workload depending on the player's positions and to compare three ACWR calculation methods. Fifty-seven players were categorized into five groups based on their playing positions: tight five (T5), third-row (3R), number nine (N9), center, and third line defense (3L). The coupled and uncoupled rolling averages (RA), as well as the exponentially weighted moving average ACWR method, were employed to compute measures derived from GPS data. Changes throughout the pre-season were assessed using the one-way and two-way analysis of variance. The results revealed that N9 covered significantly greater distances and exhibited higher player load compared to T5 and 3L [p < 0.05, effect size (ES) = 0.16-0.68]. Additionally, 3L players displayed the highest workload across various measures, including counts of accelerations and decelerations (>2.5 m s<sup>-2</sup>), accelerations (>2.5 m s<sup>-2</sup>), acceleration distance (>2 m s<sup>-2</sup>), high-speed running (>15 km h<sup>-1</sup>), very high-speed running (>21 km h<sup>-1</sup>, VSHR), sprint running (>25 km h<sup>-1</sup>, SR) distance. When using coupled RA ACWR method, centers exposed significantly greater values to T5 (p < 0.05, ES = 0.8) and 3R (p < 0.05, ES = 0.83). Moreover, centers exhibited greater (p < 0.05, ES = 0.67-0.91) uncoupled RA ACWR values for VHSR and SR than T5 and 3R. When comparing the three ACWR methods, although significant differences emerged in some specific cases, the ES were all small (0-0.56). In light of these findings, training should be customized to the characteristics of players in different playing positions and the three ACWR calculation methods can be considered as equally effective approaches.

HeliyonVol. 10(17)
Université de Pau et des Pays de l'Adour (FR), Université Rennes 2 (FR), Fédération française de cardiologie (FR), École Normale Supérieure de Rennes (FR), East China Normal University (CN)
Openalex Percentile: Top 15%
Sports Performance and Training
1.91
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