# Structured Summarization of League of Legends Match Data Optimized for Large Language Model Input
## 1 Introduction  
The exponential growth of the esports industry has transformed competitive gaming into a global phenomenon. Titles like League of Legends [1] have established extensive professional scenes, attracted millions of viewers, and generated a significant economic impact [2, 3]. As competition intensifies, teams, analysts, and coaches increasingly rely on match data to evaluate performance, plan strategies, and study opponents. These analyses often uncover trends and patterns that are difficult to observe without computational tools [4]. However, the large volume and complexity of these data present significant challenges. A single match of League of Legends can generate detailed JSON records of player actions, in-game events, and time series data, often exceeding 100 kilobytes in size [5]. This makes manual analysis and automated processing both time-consuming and computationally demanding. Recent advances in large language models (LLMs) have opened new possibilities for automated analysis of esports data. These models excel at natural language comprehension, summarization, and generating expert-like commentary [6]. However, a key limitation of most LLMs is their restricted input size, typically ranging from 4096 to 8192 tokens. While extended-context models like GPT-4o support up to 128,000 tokens [7], the detailed JSON records from a single League of Legends match can still approach or exceed this limit in complex scenarios. Existing summarization methods, such as manual extraction or adaptive data chunking [8; 9], lack structured and quantitative identification of high-impact events critical to rigorous esports analysis. Current approaches do not systematically utilize measurable momentum metrics to identify pivotal in-game events, creating a clear research gap. The summarization techniques employed in esports analytics draw theoretically from structured data reduction and summarization principles from the information retrieval domain, which emphasize identifying events based on measurable momentum and probability metrics [10; 11]. Empirical studies have shown that accurately representing momentum shifts enhances the interpretability of match summaries [12; 13], a capability inadequately addressed by simplistic chunking methods. Hence, this study addresses two research questions. (1) Can structured summarization effectively reduce the data size while maintaining analytical accuracy for esports analysis? (2) Does integrating momentum-based win probability metrics objectively enhance the identification of pivotal game events for LLM-based analytics? We propose the League of Legends Match Data Compactor, a tool designed to transform extensive match data into concise summaries suitable for LLM analysis. LoL-MDC systematically extracts the explicitly defined and quantifiable information, including match metadata, player and team statistics, simplified timeline views, and key events. The key events are identified algorithmically using a measurable advantage metric--defined as the cumulative difference between team-level gold or experience scores computed at each minute--and a corresponding Pythagorean expectation-based winning probability model. This structured and reproducible approach objectively prioritizes events based on the quantifiable momentum impact, ensuring analytical precision and clarity. Unlike general adaptability claims, this method's applicability to other esports titles can be empirically validated by substituting game-specific metrics (e.g., net worth in Dota 2 or objective control in Valorant), though empirical validation beyond League of Legends remains a subject for future research. Importantly, the primary contribution of the LoL-MDC extends beyond a mere reduction in data size. Its structured approach using momentum and probability metrics directly addresses the ambiguity and disorganization inherent in raw JSON match logs, improving the correctness and interpretability of analytical insights generated by LLMs.