Jorge A. Gómez

This study describes the weekly variations of well-being ratings relative to fatigue, stress, delayed-onset muscle soreness, sleep quality, and Hooper questionnaire throughout the season. In addition, the well-being variables for the playing position in different moments of the season were discussed. Twenty-one elite young soccer players U17 took part in this study. From the beginning of the pre-season, well-being status was monitored daily by the HQ method throughout 36 weeks, including four periods: pre-season, early-season, mid-season, and end-season. Players trained at least 3 times per week throughout the season. The main outcome was that, in weeks 33 and 28, the highest [wFatigue: 15.85 ± 3.38 arbitrary units ; wHQ: 48.86 ± 9.23 AU] and the lowest wFatigue and wHQ occurred, respectively, although the lowest level of wDOMS happened in week 28, while the highest wDOMS was observed in week 5. The highest wSleep and wStress were observed in weeks 8 and 34, respectively, while the lowest wSleep and wStress were marked in week 29 coincidentally. In the HQ between every weekday, except recovery day, and the day of the match, considerable highest HQ was only revealed in 2 days after MD in contrast to overall team comparison. In the present study, we observed that the well-being changes between different phases of the season as well as between weeks and days of the week with the MD are significant. These results provide a great point of view for coaches and practitioners about well-being variations over a season in elite youth soccer level. As a result, coaches will be more aware about non-functional overreaching and taking measures to prevent it.

Most growth models for complex networks consider networks comprising a single connected block or island, which contains all the nodes in the network. However, it has been demonstrated that some large complex networks have more than one island, with an island size distribution obeying a power-law function Is~s-α. This paper introduces a growth model that considers the emergence of islands as the network grows. The proposed model addresses the following two features: the probability that a new island is generated decreases as the network grows and new islands are created with a constant probability at any stage of the growth. In the first case, the model produces an island size distribution that decays as a power-law Is~s-α with a fixed exponent α=1 and in-degree distribution that decays as a power-law Qi~i-γ with γ=2. When the second case is considered, the model describes island size and in-degree distributions that decay as a power-law with 2