Finding the 'I' in team: development of a snapshot individual performance metric

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.