Measuring Skill Level and Optimizing Player-Matching Algorithms in Online Games
AI and Analytics for Business is pleased to announce a truly unique data set from a major international video gaming company, which will allow unprecedented insight into the habits of online gamers. The data arises from play on a single multiplayer game and includes: game usage, game play, and historic behavioral data for 9.5 million users who played 882,000 unique game rounds. Complete longitudinal tracking is done for two cohorts of players, covering every round played by each cohort member, as well as all the players they ever played with, from product launch to March 2014.
The data sponsor believes that happy players will play more and buy more – we want to help them develop new methods to test this hypothesis and develop operational improvements based on these findings. More specifically, the sponsor believes players are happiest when matched with others of similar skill level. Traditional skill-matching algorithms currently used in games do not take into account improvements in technology or detailed playing behavior that is available from today’s multiplayer games. The sponsor seeks next-generation skill assessment tools, as well as practical solutions to optimizing large-scale matching algorithms to reduce wait times for new games.
The project sponsor is open to other avenues of research, including but not limited to: social networking in online multiplayer games, online game behavior choices, or platform-based player segmentation.
Note: This Research Opportunity remains open for proposal submissions. Interested researchers can submit proposals online through the Archived Proposal Submission Portal. Researchers are encouraged to review proposal submission guidelines before submitting their proposal. Additional questions can be directed to email@example.com.
GRANTEES OF THE DATA:
FUN AND GAMES: A STRUCTURAL APPROACH TO ROLE, SKILL, AND HABIT BASED MATCHING
Jai Subrahmanyam, Yale University
Alex Smolin, Yale Univeristy
K Duhir, Yale University
OPTIMAL LEARNING OF PLAYER SKILL AND BEHAVIOR IN ONLINE GAMING
Ilya Ryzhov, University of Maryland
Shawn Mankad, University of Maryland
DISCOVERY OF LATENT PLAY STYLES FOR IMPROVED GAME MATCHING AND PREDICTION
Shane Jensen, The Wharton School
Aline Normoyle, University of Pennsylvania
A STAGE-BASED MODEL OF SHARED GOAL PURSUIT: A STUDY FO GAMING PLATFORMS
Szu-Chi Huang, Stanford University
Pinar Yildirim, The Wharton School
Mariam Hambarchyan, Stanford University
LEARNING VS. ENJOYMENT – A FORWARD-LOOKING MODEL OF PLAYERS’ DECISIONS IN THE VIDEO GAME INDUSTRY
Hema Yoganarasimhan, University of Washington
Scott Shriver, Columbia University
“JUST ONE MORE LEVEL”: LEVERAGING SKILL AND ENGAGEMENT TO MAXIMIZE PLAYER SATISFACTION AND GAME REVENUE IN ONLINE VIDEO GAMES
Stefanus Jasin, University of Michigan
Yan Huang, University of Michigan
Puneet Manchanda, University of Michigan
HOW TO MAKE A CUSTOMER HAPPY? IMPROVING MATCHINGS IN MULTI-PLAYER VIDEO GAMES TO GENERATE LONG-TERM POSITIVE GAME EXPERIENCES
Joe Cox, Portsmouth Business School
Daniel Kaimann, University of Paderborn
Nadja Maraun, University of Paderborn
KEEP WINNING OR STOP LOSING? THE EFFECTS OF CONSUMPTION OUTCOMES ON CUSTOMER ENGAGEMENT ON EXPERIENTIAL PRODUCTS
Katie Yang, The Wharton School
Tong Lu, The Wharton School
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