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Big Data Meets Team Expertise in a Dynamic Task Environment

2016; SAGE Publishing; Volume: 60; Issue: 1 Linguagem: Inglês

10.1177/1541931213601036

2169-5067

Matthew-Donald D. Sangster, David Mendonça, Wayne D. Gray,

Data Visualization and Analytics

Objective; 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. Conclusions: The results point to the potentially substantial benefits of large-scale studies of teamwork, and–in parallel–to 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 into team and individual performance.