@conference{sangster16hfes,
title = {Big Data Meets Team Expertise in a Dynamic Task Environment},
author = {Sangster, Matthew-Donald D. and Mendonca, David J. and Gray, Wayne D.},
year = {2016},
date = {2016-09-21},
booktitle = {Proceedings of the Human Factors and Ergonomics Society Annual Meeting},
volume = {60},
number = {1},
pages = {158-162},
publisher = {Sage },
abstract = {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.},
keywords = {dynamic task environment, League of Legends, LoL, team, team expertise},
pubstate = {published},
tppubtype = {conference}
}
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.
