Implementation of user playstyle coaching using video processing and statistical methods in league of legends
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Implementation of user playstyle coaching using video processing and statistical methods in league of legends Shinyoung Kim 1 & Dohyeon Kim 2 & HyungGeun Ahn 3 & Byeongtae Ahn 4 Received: 26 February 2020 / Revised: 15 May 2020 / Accepted: 21 July 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract
Recently, the game market is growing rapidly with the growth of e-sports. In particular, the market for League of Legends games continues to grow. Massively Online Battle Arena (MOBA) games have grown to become a massive industry, projected to reach over 140 billion USD worth of market value. Many users learn the League of Legends game, but their skills do not improve. Current analysis of the player’s manual playback through visual media such as video is the most common method. Therefore, this paper extracts data from gameplay videos and analyzes intuitive gameplay “styles” in the popular MMO game League of Legends to provide coaching-specific information. we were able to classify whether the player is cooperative and aggressive, but if additional information which we did not extract nor process like map vision were taken into account, Big Data and Machine Learning could come into play. Keywords LoL game . Video processing . Coaching education . Deep learning . Playstyle
* Byeongtae Ahn [email protected] Shinyoung Kim [email protected] Dohyeon Kim [email protected] HyungGeun Ahn [email protected]
1
Boin High School, 18 Ogeum-ro 49-gil, Ogeum-dong, Songpa-gu, Seoul, South Korea
2
PlayAuto Inc., 18 Ogeum-ro 49-gil, Ogeum-dong, Songpa-gu, Seoul, South Korea
3
Sungkyunkwan University, 25-2 Sungkyunkwan-ro, Myeongnyun 3(sam)ga-dong, Jongno-gu, Seoul, South Korea
4
Liberal & Arts College, Anyang University, Anyang, South Korea
Multimedia Tools and Applications
1 Introduction Massively Online Battle Arena (MOBA) games have grown to become a massive industry, projected to reach over 140 billion USD worth of market value [31]. Figure 1 is League of Legends, the game leading the MOBA market with its 80 million monthly users, boast over 99.6 million unique viewers tuned in to watch Worlds 2018, one of League of Legends’ most prominent event, surpassing Super Bowl’s 98.2 million. Figures 2 and 3 respectively show an optimistic future for the industry. A prime reason how League of Legends was able to be successful is related to the competitiveness of the game: An in-game “rank” system, similar to the elo system of chess, means one can constantly see how they fare compared to players of similar skill [11, 25, 32]. In an effort to raise their skill, effectively also raising their “rank”, players will use various tools to their advantage. One of the popular tools players use is “OP.GG”, which provides players with statistics on individual games, and additional information related to League of Legends. Originally a startup, “OP.GG” received attention from various accelerators, leading to investments exceeding 10 million USD in total [1–3, 9]. However, as much as sites such as OP.GG were ab
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