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The study of teammates' interaction on team sports has been growing in the last few years. Nevertheless, no specific software has been developed so far to do this in a user-friendly manner. Therefore, the aim of this study was to introduce a software called the Performance Analysis Tool that allows the user to quickly record the teammates' interaction and automatically generate the outputs in adjacency matrices that can then be imported by social network analysis software such as SocNetV. Moreover, it was also the aim of this study to process the data in a real-life scenario, thus the seven matches of the German national soccer team in the FIFA World Cup 2014 were used to test the software and then compute the network metrics. A dataset of 3032 passes between teammates in seven soccer matches was generated with the Performance Analysis Tool software, which permitted a study of the network structure. The analysis of variance of centrality metrics between different tactical positions was made. The two-way multivariate analysis of variance revealed that the strategic position (g = 1:305; F = 24.394; p = 0.001; h 2 p = 0:652; large effect size) had significant main effects on the centrality measures. No statistical differences were found in the phase of competition (g = 0:003; F = 0.097; p = 0.907; h 2 p = 0:003; very small effect size). The network approach revealed that the German national soccer team based their attacking process on positional attacks and not in counterattack , and the midfielders were the prominent players followed by the central defenders. The Performance Analysis Tool software allowed the user to quickly identify the teammates' interactions and extract the network data for process and analysis.

The aim of this study was to analyse the variance of different competitive leagues, score status, and tactical position in the centrality levels of degree prestige, degree centrality and page rank in football players. A total of 20 matches from the Spanish La Liga League (10 matches) and English Premier League (10 matches) were analysed and codified in this study. In this study only the top four teams and their opponents per each competitive league were analysed. A total of 14,738 passes between teammates were recorded and processed. The multivariate MANOVA revealed statistical differences in centrality among tactical positions (λ = 0.958; F(15,1212) = 37.898; p-value = 0.001; η 2 = 0.319; Moderate Effect Size). Midfielders had the greatest centrality values, followed by the external and central defenders. The lowest values of centrality were found in goalkeepers and forwards. No statistical differences were found in centrality between different competitive leagues (λ = 0.001; F(3,402) = 0.050; p-value = 0.985; η 2 = 0.001; Very Small Effect Size) and score status (λ = 0.003; F(6,806) = 0.175; p-value = 0.983; η 2 = 0.001; Very Small Effect Size).

Background: Relational satisfaction of spousal/partner informal caregivers of people with multiple sclerosis (MS) is important for continued care and support. Previous studies have examined relational satisfaction in terms of well-being and quality of life of informal caregivers. Based on the Rusbult investment model, we directly studied the relational satisfaction of spousal/partner informal caregivers of individuals with MS. In doing so, we investigated possible effects that commitment to relationship, caregiving burden, and prorelational behavioral tendencies might have on relational satisfaction. Methods: Nine hundred nine adult spousal/partner informal caregivers of people with MS completed measures of relational satisfaction (Kansas Marital Satisfaction Scale), commitment to relationship (15-item commitment measure), caregiving burden (Zarit Burden Interview), and prorelational behavioral tendencies (adapted Prosocial Tendencies Measure). Participants also provided demographic information (age, sex, duration and type of relationship [spouse, partner]). Results: Structural equation modeling highlighted commitment to the relationship as the strongest predictor of relational satisfaction. Caregiving burden was found to affect relational satisfaction directly and through commitment to relationship. Prorelational behavioral tendencies were found to affect less relational satisfaction. Conclusions: Commitment to relationship, namely, intent to persist, had the highest positive effect on satisfaction. Caregiving burden was found to have a two-way negative relationship to commitment to relationship. These findings suggest that specialists should enhance the intent-to-persist aspect of commitment because it seems to have an alleviating effect regarding caregiving burden (which itself negatively affects relational satisfaction).