Thinking Style and Team Competition Game Performance and Enjoyment

Hao Wang, Hao Tsung Yang, Chuen-Tsai Sun

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

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.

Original languageEnglish
Article number7182769
Pages (from-to)243-254
Number of pages12
JournalIEEE Transactions on Computational Intelligence and AI in Games
Volume7
Issue number3
DOIs
StatePublished - 1 Sep 2015

Keywords

  • Matchmaking
  • player data mining
  • player modeling
  • player satisfaction
  • thinking style

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