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Splitting the sample of all teams (with 2 to 60 members) into four specific subsamples was a prerequisite for comparing the network patterns of teams of different sizes.
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Fig 2 highlights this relationship between group size and team performance.
Teams within Travian constantly form and dissolve.. In the first phase of the game, the number of alliance formations exceeds the number of dissolutions, which finds its peak at the beginning of the middle phase of the game. At this point a massive reorganization process takes place. In the later phases of the game, the number of team dissolutions exceeds the number of new formations, so the number of active teams steadily decreases.
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Fig 3). Further, team dynamics play a crucial role in this second phase and we assumed that they would be strongly reflected in the observed network patterns.
A schematic representation of the observation periods taken from the mid game phase, starting with day 110 and ending with day 250.. Our modified moving window approach (advancing 30 days at a time) integrated all communication and support relationships that comprised a full 60-day period.
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With this in mind, we decided to use a moving window, in which we aggregated all communication and support relationships over 60 days, and also moved the window forward by 30 days at a time (see Fig 4).
Support networks.. Correlation of each measure with team performance for the groups of small-, mid-, and large-size teams as well as all teams.
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In the following section, we will present the results in the form of correlation tables (Table 3) combined with scatter plots (Figs 5 and 6), which are primarily intended to provide a better understanding of the context and the specifics of the data.
Communication networks.. Correlation of each measure with team performance for the groups of small-, mid-, large-size teams as well as all teams.
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In the following section, we will present the results in the form of correlation tables (Table 3) combined with scatter plots (Figs 5 and 6), which are primarily intended to provide a better understanding of the context and the specifics of the data.