# Quantifying Sub-Elite Youth Football Weekly Training Load and Recovery Variation
## 4 Discussion
### Inter-Playing Positions Analysis  
The external and internal training loads were significantly different between playing position in the present study (Table 4). These differences were verified in rHSR and the average sprint distance between FW vs. CD players (minimum to moderate effect). Additionally, the internal training load presented significant differences between WM and FW players (minimum effect). The inter-positional variation was confirmed in a few studies addressing adult male and female professional football [23; 24; 25; 40; 46]. In youth football, the influence of playing position on physical and physiological performance during competition is well documented [27; 63; 64; 65; 66; 28]. Although the positional role has been analyzed in training environments, the training load analysis was focused on constrained training tasks [29; 30; 69; 70]. Therefore, it is important to know the influence of positional role in the weekly training load. Comparing elite and sub-elite football academies is also an important research gap which studies that examine accumulated weekly training load did not include in playing position analysis [15; 31; 32; 33]. In the present study, the TQR score did not present significant differences within playing position groups. In contrast, the influence of playing position on the recovery status in the adult training football has been documented [20; 71]. The results of this study seem to suggest that the influence of playing positions on the weekly training load and recovery status is unclear. A possible explanation could be related to the training tasks, which may not be representative of the positional role specificity [53]. However, future investigations may focus on high-demanding variations. Our findings show significant differences in two high-intensity variables (i.e., rHSR and average sprint distance). Additionally, the weekly training load quantification should consider the game model and representative game-based situations to promote playing position specificity [10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55].  
Our study presented some limitations and the results should be interpreted with caution. First, the training data included only 2-week monitoring due to the constraints arising from the COVID-19 pandemic. Wherefore, the sample size was rather small, which reduces the understanding about training load and recovery variations across a competitive season. Additionally, the training data addressing only one sub-elite football academy cannot be extended to other teams and regions. This way, longitudinal samples should be considered in further investigations to examine seasonal variation. Second, biological maturation was not considered, which could bias the age group analysis [72; 73]. Third, the current research analyzed overall training sessions rather than considering the training mode for each training exercise [29; 30; 40; 46; 69; 70]. Integrating accumulated training load and matching data should be considered in further investigations on youth football. Indeed, there is a need for studies considering the effects of different week schedules (i.e., one-, two- and three-game week). The current literature analyzes the accumulated training load in professional football [74]. Furthermore, speed and acceleration thresholds in the present experimental approach were based on elite gold-standard guidelines. Future research should focus on the adjustment thresholds for elite and sub-elite youth football.