Other works have focussed on analysing and visualizing spatial and/or spatio-temporal player behaviour for the purpose of informing game design or -development. In relation to the work presented here, trajectories in 3D games have been used to direct AI bot movement in games, covering both unit movement in RTS games and bot movement in FPS games. A recent review is provided by Drachen & Schubert. Outside the MOBA domain, a significant amount of work has been done on spatio-temporal behaviour analysis in games. Harrop investigated the nature of rules in DotA, and noted that players use difference “truce call” to negotiate rules and the maintenance of fair play in the game.
used data from the DotA 2 web community to investigate teamwork, concluding for example that more experienced teams win more often, and that playing with in-game friends as opposed to pick-up teams increases the chance of winning.
The authors noted that different roles in the game require different kinds of leadership. On the topic of MOBAs, but not focussed on in-game behavioural analysis, Nuangjumnonga and Mitomo analysed results from a close-ended survey to examine potential correlations between behaviour and leadership development in the MOBAs DotA 2 and Heroes of Newerth. The authors attributed features to the graphs using frequent sub-graph mining which allowed them to describe how different combat tactics contributed to team success in specific situation. defined specific roles of DotA 2 players in the game, modelled combat as a sequence of graphs and used this repre- sentation to extract patterns that predict successful outcomes of individual fights in the game as well as matches with an 80% prediction accuracy.