Institute of Electrical and Electronics Engineers Inc.;Piscataway: IEEE
摘要:
摘要： 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. 其他題名： TCIAIG 出版者： Piscataway: IEEE 出版日期： 2015-09 出處： IEEE transactions on computational intelligence and AI in games., 2015-09, Vol.7 (3), p.243-254 資源來源： IEEE Electronic Library (IEL) 版權： Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Sep 2015 識別號： ISSN: 1943-068X 識別號： ISSN: 2475-1502 識別號： EISSN: 1943-0698 識別號： EISSN: 2475-1510 識別號： DOI: 10.1109/TCIAIG.2015.2466240 識別號： CODEN: TCIARR
