Visualizing the recorded logs of users' UI operations is a promising way for quantitatively understanding the interaction patterns. Conventionally, the game experts achieve their purposes through intensive user studies with target players or iterative UI design processes, which can not capture interaction patterns of large-scale individual players. Understanding how players interact with the mobile game app on smartphone devices is important for game experts to develop and refine their app products. Finally, we validate the usability of our system by proving the identified patterns are representative in snowballing or comeback matches in a one-month-long MOBA tournament dataset. We further demonstrate a workflow of leveraging human analyzed patterns to improve the scalability and generality of match data analysis. Our system can reveal players' strategies and performance throughout a single match and suggest patterns, e.g., specific player' actions and game events, that have led to the final occurrences. We apply novel visualization techniques in conjunction with wellestablished ones to depict the evolution of players' positions, status and the occurrences of events.
Cool moba games trial#
We follow a user-centered design process developing the system with game analysts and testing with real data of a trial version MOBA game from NetEase Inc. In this paper, we present a visual analytics system to help game designers find key events and game parameters resulting in snowballing or comeback occurrences in MOBA game data. In addition, the huge amounts of MOBA game data are often heterogeneous, multi-dimensional and highly dynamic in terms of space and time, which poses special challenges for analysts. Although it is easy to identify these two types of occurrences, game developers often find it difficult to determine their causes and triggers with so many game design choices and game parameters involved. Abstract-To design a successful Multiplayer Online Battle Arena (MOBA) game, the ratio of snowballing and comeback occurrences to all matches played must be maintained at a certain level to ensure its fairness and engagement. (f) Player Billing Radar View represents the statistical information of each player. (e) Tactic Comparison View (Left) unfolds the temporal dynamics of all the tactical actions in two camps while Equipment Evolution View (Right) shows the equipment evolution hierarchies. (d) Resource Time Sequence View displays the accumulated resources and changes in resources of each player. (c) Tactic Geographical Timeline View presents details of players' behavior in the time period of interest. (b) Trajectory View simulates the game replay. (a) Trend View discloses the trend of game play during a match. Finally, we validate the usability of our system by proving the identified patterns are representative in snowballing or comeback matches in a one-month-long MOBA tournament dataset.Ī match with comeback occurrence. We apply novel visualization techniques in conjunction with well-established ones to depict the evolution of players' positions, status and the occurrences of events. To design a successful Multiplayer Online Battle Arena (MOBA) game, the ratio of snowballing and comeback occurrences to all matches played must be maintained at a certain level to ensure its fairness and engagement.
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