StratIncon Detector: Analyzing Strategy Inconsistencies Between Real-Time Strategy and Preferred Professional Strategy in MOBA Esports

2025-03-12

AI Summary

This paper addresses the challenge of identifying and attributing inconsistencies between real-time strategic decisions and expert-preferred strategies in MOBA professional esports. We propose the first quantitative framework for “strategic inconsistency” analysis. Methodologically, it integrates action-sequence modeling, temporal graph neural networks, and expert knowledge distillation to construct interpretable strategy-comparison embeddings and a visualization system supporting causal attribution and collaborative insight generation. Our key innovation lies in tightly coupling real-time behavioral modeling with domain-specific expertise, enabling automated inconsistency detection, root-cause localization, and association with match-level outcomes. Evaluated through a 24-participant user study and validation with professional players, our framework improves inconsistency identification efficiency by 2.3× and attribution accuracy by 37% over baselines. It significantly enhances post-match review depth and team-level strategic optimization capabilities.

Technology Category

• Reasoning under Uncertainty: Sequential Decision Making
• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Machine Learning: Mixture of Experts (MoE)

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

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.

Problem

• Analyzing inconsistencies between real-time and preferred strategies in MOBA esports.
• Quantifying differences and understanding disparities in professional match strategies.
• Establishing causal links between strategic decisions and game outcomes.

Innovation

• Visual analytics system for strategy analysis
• Detects real-time and predicts preferred strategies
• Extracts human factors impacting game phases