Metagame Analysis Laboratory
Esports & Games across all specialties
We are Metagame Analysis Laboratory, an interdisciplinary Scientific Research Laboratory for Esports & Games in the Department of Computer Science at Yonsei University. We investigate novel concepts that underlie the complexities of Esports & Games.
Professor
Jimoon Kang
Research Professor, Department of Computer Science, Yonsei Univerisity.
Analyst, T1 (ID: Gisepa(기세파)).
E-mail: kangjimoon@yonsei.ac.kr
Experience
Analyst, KT Rolster, 2020-2021.
King-zone DragonX, 2019.
Postdoctoral Researcher, University of Notre Dame, 2020-2021.
Education
PhD in Chemical and Biological Engineering, Seoul National University, 2020.
BS in Chemical and Biological Engineering, Seoul National Univerisity, 2013
Research
Primary Areas of Interest
Metagame Analysis in League of Legends
Metagames are the perceived optimal or dominant playing strategy. They are set of popular strategies or overarching way of play that appears optimised for an individual player or team based on both their perceived strengths and weaknesses as well as those of their respective opponents, using information contained both in and outside the game and its surrounding environment. They are never created in isolation, and rather they are part of a shifting ecosystem of highly public competitive play.
Cognitive Function of Esports player
Cognitive function of gamers refer to the collective cognitive characteristics of gamers who plan and execute appropriate input actions in response to input tasks to be performed in games.
Esports players show shorter reaction time, higher accuracy and precision than general gamers, but there is no quantitative study on Esports player. Based on the modeling and testing tools in the cognitive function research in existing sports, we develops a cognitive function measurement program by evaluating and analyzing the objective cognitive ability of various Esports players.
Aging curve of Esports player
The performance of athletes is significantly affected by age. The performance of athletes grow and decline with age, and the expression of this degree as a function is called the aging curve. We predict and evaluate the age-related changes of players based on modeling the position and initial performance of Esports players.
The game of Go after Alphago
One of the most surprising events in AI over the past decade is the advent of AlphaGo developed by Alphabet's Google DeepMind. We study the impact of AI on Go players with the changes in play style and the changes in age-specific performance.
Publications
CHI 2022 Papers (Revision ongoing) - How AI-based Training Changed Performance Development of Go Players, 2022
In this paper, we analyzed how the training methods, game play, and age trend of professional Go players have changed since the advent of AlphaGo, the first AI to defeat a human Go world champion. This paper got 'Revise and resubmit' in the ACM Conference on Human Factors in Computing Systems (CHI), the most prestigious in the field of human–computer interaction and the top-ranked conferences in computer science.
eSports & Games Studies - Aging curve of eSports players in League of Legends, 2021
In this paper, the effect of the eSports player's age on the performance in four major regional leagues of League of Legends (LCK, LPL, LEC, and LCS) was investigated from spring 2016 to summer 2020.
Community
Meta Analysis
