Physics

League

Knowledge management

Scarcity

Operating system

Astronomy

Artificial intelligence

Psychology

Economics

Microeconomics

Profiling (computer programming)

Data science

Popularity

Computer science

Social psychology

Despite the increasing popularity of electronic sports (eSports), there is still a scarcity of academic works exploring the playing behavior of teams. Understanding the features that help to discriminate between successful and unsuccessful teams would help teams improving their strategies, such as determine performance metrics to reach. In this paper, we identify and characterize team behavior patterns based on historical matches from the very popular eSpor League of Legends web API. By applying machine learning and statistical analysis, we clustered teams' performance and investigate for each cluster how and to what extent these features have an influence on teams' success and failure. Some clusters are more likely to have winning teams than others, the results of our study helped to discover the characteristics that are associated with this predisposition and allowed us to model performance metrics of successful and unsuccessful team profiles. At all, we found 7 profiles in which were categorized into four levels in terms of winning team proportion: very low, moderate, high and very high.

Profiling Successful Team Behaviors in League of Legends