Thinking Style and Team Competition Game Performance and Enjoyment

Abstract

Almost all current matchmaking systems for team competition games based on player skill ratings contain algorithms designed to create teams consisting of players at similar skill levels. However, these systems overlook the important factor of playing style. In this paper, we analyze how playing style affects enjoyment in team competition games, using a mix of Sternberg's thinking style theory and individual histories in the form of statistics from previous matches to categorize League of Legend (LoL) players. Data for approximately 64 000 matches involving 185 000 players were taken from the LoLBase website. Match enjoyment was considered low when games lasted for 26 min or less (the earliest possible surrender time). Results from statistical analyses indicate that players with certain playing styles were more likely to enhance both game enjoyment and team strength. We also used a neural network model to test the usefulness of playing style information in predicting match quality. It is our hope that these results will support the establishment of more efficient matchmaking systems.Index Terms-Matchmaking, player data mining, player modeling, player satisfaction, thinking style. 1943-068X

Setting the scientific stage for esports psychology: a systematic review

“…Additionally, it was found that the successful teams in LoL were those that showed the highest scores on the Transactive Memory System Scale, which measures a group's ability to learn, remember, and communicate knowledge relevant to the group (i.e., shared knowledge; Kahn & Williams, 2016). Moreover, Wang, Yang, and Sun (2015) found that those teams that possessed at least one player with a global-liberal playing style, that is, a player who assisted teammates (i.e., collaborative style), had significantly higher win rates than teams without this type of player.…”

Relevant Independent Variables on MOBA Video Games to Train Machine Learning Algorithms

“…A limitation commonly mentioned in research is how to introduce into the models the synergies that must exist between players of the same team. Related to this question the category of team composition was created for those papers that seek objectives related to the roles of each player within a team [6] [50], the analysis of player behavior in the composition of a team [7] A total of 45 different independent variables were identified in the works within this category. Table 8 presents those used in more than one research.…”

Untitled

“…Forecasting focus towards the predictions of the future based past and present data [3,14]. The two terms are focussed on the forecasting are risk and uncertainty.…”