Key Indicators and Data-Driven Analysis Methods for Game Performance Optimization

Abstract

With the continuous enhancement of user experience requirements in the gaming industry, performance optimization has become a core component of modern game development. However, traditional experience-driven tuning methods and isolated performance-based optimization strategies are no longer sufficient to address the increasingly heterogeneous device environments, diverse usage scenarios, and the growing complexity of user interaction patterns. As game ecosystems evolve toward higher fidelity, larger scale, and cross-platform deployment, developers urgently need systematic and data-driven mechanisms to identify performance bottlenecks and ensure stable, high-quality gameplay experiences. This paper focuses on the construction of a comprehensive key indicator system for game performance optimization, the design of efficient data collection and preprocessing pipelines, and the development of data-driven performance tuning strategies. By integrating multi-dimensional data sources-including frame rate stability, resource consumption metrics, latency information, and user-perceived smoothness-the study establishes a new comprehensive discriminant model that evaluates performance by jointly considering technical indicators and user experience indicators. This dual-dimension assessment enables a more balanced and accurate understanding of performance quality. Furthermore, the paper proposes a complete empirical workflow for cross-platform performance improvement. By incorporating visualization-based diagnostic tools, real-time feedback mechanisms, and adaptive optimization loops, the framework supports rapid identification of performance issues, iterative refinement, and systematic optimization decisions. The approach not only improves performance tuning efficiency but also enhances the scalability and transparency of the process, providing actionable guidance for optimizing game performance across devices and platforms.