Matthew-Donald Sangster

 

Matthew-Donald Sangster

Sage 4101

February 24, 2016 12:00 PM - 1:30 PM

This research employs large-scale data from a massively multiplayer online game to examine the links between the composition, processes and outcomes of teams operating in high tempo, data-rich environments. Background: Research on the performance of teams–particularly over long time scales–is often expensive and time-consuming. But Big Data from competitive, team-based games can mitigate these costs. Methods: Data visualization techniques are used to explore team data harvested from publicly accessible sources for the online game League of Legends., one of the most popular such games in the world. Results: The exploratory results suggest potentially complex relationships between team composition, processes and outcomes, and in particular how team composition and process may unfold over longer time spans than are possible to examine in typical studies. Conclusions: The results point to the potentially substantial benefits of large-scale studies of teamwork, and–in parallel–of the need for the development of tools, techniques and measures to bring Big Data to bear in teamwork studies. Application: This work demonstrates the feasibility of exploring online gaming data for new insights on team and individual performance.

 

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