Finding the 'I' in team: development of a snapshot individual performance metric
Authors
Sangster, Matthew-Donald
ORCID
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Other Contributors
Gray, Wayne D., 1950-
Mendonça, David
Dunn, Stanley
Mendonça, David
Dunn, Stanley
Issue Date
2017-05
Keywords
Cognitive science
Degree
MS
Terms of Use
Attribution-NonCommercial-NoDerivs 3.0 United States
This electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.
This electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.
Full Citation
Abstract
Though both individual and team performance are widely studied, evaluating individual performance within the context of the team usually relies on relative measures of performance. However, in order to apply much of the findings and theories from individual performance literature it is crucial to develop an absolute measure of individual performance--a "Snapshot" individual performance metric. With such a measure, it becomes possible to determine the contribution of a individual towards the goals of the team. This paper uses Big Data (1.9 million records from 539 thousand matches) from League of Legends, a widely popular competitive team game to look at individual performance based on the overall priorities different members of a team should have. This research applies exploratory factor analysis and logistic regression to determine the latent factor structure of behavioral variables that are predictive of team performance. Through this, we develop the first steps of establishing an individual priority-based measure of performance that can be used to evaluate individual performance for a single observation.
Description
May 2017
School of Humanities, Arts, and Social Sciences
School of Humanities, Arts, and Social Sciences
Department
Dept. of Cognitive Science
Publisher
Rensselaer Polytechnic Institute, Troy, NY
Relationships
Rensselaer Theses and Dissertations Online Collection
Access
CC BY-NC-ND. Users may download and share copies with attribution in accordance with a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License. No commercial use or derivatives are permitted without the explicit approval of the author.