INTRODUCTION: Professional rugby union players can improve their performance by engaging in small-sided games (SSGs), which simulate the movement patterns of the game [1]. 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 field sizes (five sessions), SSG rules (five 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. Considering the varying training participants and field areas, this study employed a two-way analysis of variance (ANOVA) to investigate the interactions among these factors. The Mann-Whitney U test, Kruskal-Wallis H-test and its subsequent post hoc comparison procedure (Dunns post hoc analysis) were applied due to the non-normal distribution of the GPS metrics, the heterogeneity of the variance and the comparison of SSG models and OGs. The statistical significance was set at pgeneity of the variance and the comparison of SSG models and OGs. The statistical significance was set at p<0.05. Effect size (ES) was evaluated using Cohen`s d and partial eta squared (np²) along with a 95 % confidence interval. Cohen`s d=0.2, 0.5, and 0.8, np²=0.01, 0.06 and 0.14 correspond to small, medium and large effects respectively. RESULTS: The results showed that backs had greater workload than forwards. Less space and number of players SSG (4 vs. 4, 660m2) 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 (p<0.001, np²=0.19-0.28). Japan touch model allowed for more high-speed running training (p<0.001, np²=1.52). The workload performed by SSGs can almost cover that of OGs (p<0.05, np²=0.03-0.57). CONCLUSION: This study provided insight into the impact of player numbers, field 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. 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.
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