StratIncon Detector: Analyzing Strategy Inconsistencies Between Real-Time Strategy and Preferred Professional Strategy in MOBA Esports
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866909535438897152 |
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| author | Ma, Ruofei Zhao, Yu Shao, Yuheng Yao, Yunjie Li, Quan |
| author_facet | Ma, Ruofei Zhao, Yu Shao, Yuheng Yao, Yunjie Li, Quan |
| contents | MOBA (Multiplayer Online Battle Arena) games require a delicate interplay of strategic planning and real-time decision-making, particularly in professional esports, where players exhibit varying levels of skill and strategic insight. While team strategies have been widely studied, analyzing inconsistencies in professional matches remains a significant challenge. The complexity lies in defining and quantifying the difference between real-time and preferred professional strategies, as well as understanding the disparities between them. Establishing direct causal links between specific strategic decisions and game outcomes also demands a comprehensive analysis of the entire match progression. To tackle these challenges, we present the StratIncon Detector, a visual analytics system designed to assist professional players and coaches in efficiently identifying strategic inconsistencies. The system detects real-time strategies, predicts preferred professional strategies, extracts relevant human factors, and uncovers their impact on subsequent game phases. Findings from a case study, a user study with 24 participants, and expert interviews suggest that, compared to traditional methods, the StratIncon Detector enables users to more comprehensively and efficiently identify inconsistencies, infer their causes, evaluate their effects on subsequent game outcomes, and gain deeper insights into team collaboration-ultimately enhancing future teamwork. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_09060 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | StratIncon Detector: Analyzing Strategy Inconsistencies Between Real-Time Strategy and Preferred Professional Strategy in MOBA Esports Ma, Ruofei Zhao, Yu Shao, Yuheng Yao, Yunjie Li, Quan Human-Computer Interaction MOBA (Multiplayer Online Battle Arena) games require a delicate interplay of strategic planning and real-time decision-making, particularly in professional esports, where players exhibit varying levels of skill and strategic insight. While team strategies have been widely studied, analyzing inconsistencies in professional matches remains a significant challenge. The complexity lies in defining and quantifying the difference between real-time and preferred professional strategies, as well as understanding the disparities between them. Establishing direct causal links between specific strategic decisions and game outcomes also demands a comprehensive analysis of the entire match progression. To tackle these challenges, we present the StratIncon Detector, a visual analytics system designed to assist professional players and coaches in efficiently identifying strategic inconsistencies. The system detects real-time strategies, predicts preferred professional strategies, extracts relevant human factors, and uncovers their impact on subsequent game phases. Findings from a case study, a user study with 24 participants, and expert interviews suggest that, compared to traditional methods, the StratIncon Detector enables users to more comprehensively and efficiently identify inconsistencies, infer their causes, evaluate their effects on subsequent game outcomes, and gain deeper insights into team collaboration-ultimately enhancing future teamwork. |
| title | StratIncon Detector: Analyzing Strategy Inconsistencies Between Real-Time Strategy and Preferred Professional Strategy in MOBA Esports |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2503.09060 |