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. 2024 Feb 12:15:1339137.
doi: 10.3389/fphys.2024.1339137. eCollection 2024.

Optimization of training for professional rugby union players: investigating the impact of different small-sided games models on GPS-derived performance metrics

Affiliations

Optimization of training for professional rugby union players: investigating the impact of different small-sided games models on GPS-derived performance metrics

Xiangyu Ren et al. Front Physiol. .

Abstract

Introduction: Professional rugby union players can improve their performance by engaging in small-sided games (SSGs), which simulate the movement patterns of the game. This study collected metrics related to running performance and mechanical workload and their relative values from both forward and back positions, aiming to explore the impact of different SSGs factors on athlete workload, as well as the workload difference between official games (OGs) and SSGs. Methods: The monitored GPS data were collected from SSGs with different player numbers and pitch sizes (five sessions), SSG rules (5 weeks, four sessions per week), and OGs conducted throughout the year. Additionally, the study compared changes in players' sprinting performance before and after two SSG sessions. Results: Backs had greater workload than forwards. Less space and number of players SSG (4 vs. 4, 660 m2) was conducive to facilitating training for players in acceleration and deceleration. Conversely, larger spaces were associated with improved running performance. However, the introduction of a floater had no significant impact on performance improvement. Additionally, the 7 vs. 4 model (seven players engaged with four opponents) resulted in the greatest workload during medium-hard accelerations (F = 52.76-88.23, p < 0.001, ηp 2 = 0.19-0.28). Japan touch model allowed for more high-speed running training (F = 47.93-243.55, p < 0.001, ηp 2 = 1.52). The workload performed by SSGs can almost cover that of OGs (F = 23.36-454.21, p < 0.05, ηp 2 = 0.03-0.57). In the context of ηp 2, values around 0.01, 0.06 and 0.14 indicate small, medium and large effects respectively. Discussion: However, given the significantly higher workload of SSGs and the slight decrease in sprinting performance, further research is required to examine the training patterns of SSGs. This study provided insight into the impact of player numbers, pitch size, and rules on rugby-specific SSGs. Coaches should optimize SSG setups for enhanced training outcomes, ensuring the long-term development of physical capacity, technical and tactical skills.

Keywords: constraints-led approach; external load; global positioning system; intermittent exercise; team sports.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
The arrangement of SSGs and organization of the 2022–2023 Pro D2 season.
FIGURE 2
FIGURE 2
Comparison of workload and performance changes in S-SSG and E-SSG models. (A) Comparison of TD for forwards and backs. (B) Running workload comparison for forwards and backs. (C) Mechanical workload comparison for forwards and backs. (D) Difference between pre-and post-performance test on neuromuscular function in 10 m and 20 m. S-SSG, small-sided game played on strength training day; E-SSG, small-sided game performed on endurance training day; HSR, high-speed running (>15 km.h−1); VHSR, very high-speed running (>21 km.h−1); SR, sprint running (>25 km.h−1); MA, the number of medium accelerations (>2 m.s−2); HA, the number of hard accelerations (>2.5 m.s−2); MD, the number of medium decelerations (>2 m.s−2); HD, the number of hard decelerations (>2.5 m.s−2); RHIE, repetitive high-intensity exercise. *p < 0.05, **p < 0.01, ***p < 0.001.
FIGURE 3
FIGURE 3
Effect of player numbers on performance in SSG models for forwards and backs. (A) Impact of the number of players on TD performance for forwards. (B) Impact of the number of players on HSR performance for forwards. (C) Impact of the number of players on D-MA performance for backs. (D) Impact of the number of players on D-HA performance for backs. §: Compared to 7 vs. 7, p < 0.05; †: Compared to 4 vs. 4, p < 0.05; #: Compared to 5 vs. 5, p < 0.05.
FIGURE 4
FIGURE 4
Comparison of relative performance metrics between SSG models and OGs. (A) Comparison of TD relative values (m.min−1) for S-SSG models, E-SSG models, and OGs. (B) Comparison of TD relative values (m.min−1) for different player numbers in SSG models and OGs. (C) Comparison of RHIE relative values (RHIE.min−1) for S-SSG, E-SSG, and OGs. (D) Comparison of RHIE relative values (RHIE.min−1) for different player numbers in SSG models and OGs. S-SSG, small-sided game played on strength training day; E-SSG, small-sided game performed on endurance training day; OG, official game. ‡: Compared to OG, p < 0.001; △: Compared to OG, p < 0.05.
FIGURE 5
FIGURE 5
Pairwise comparison for forwards and backs in (A, B) m.min−1, (C, D) PL.min−1, and (E, F) RHIE.min−1 about SSGs and OGs analyzed. Each node showed the sample average rank of SSGs and OGs. The black line represented significant differences among groups (p < 0.05). m.min−1: relative value of total distance, PL.min−1: relative value of player load, RHIE.min−1: relative value of repetitive high-intensity exercise.

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