# Structured Summarization of League of Legends Match Data Optimized for Large Language Model Input
## 4 Experiments
### Datasets and Experimental Set-Up  
The dataset used for the experiments consists of JSON files internally curated by the analytics team at LOLPS [28], one of the largest League of Legends statistics websites. Each match record was initially obtained from the Match-V5 endpoint of the Riot Games API [5]. A single raw response from this endpoint typically exceeds 300 kB (approximately 120,000 tokens), containing comprehensive metadata and numerous auxiliary fields, which makes it impractical for direct use with LLM prompts. To address this issue, we utilized an intermediate, internally curated version containing the variables essential for performance analytics--match metadata, per-team and per-player statistics, one-minute aggregated gold and experience (XP) timelines, and high-level event logs (objectives, multi-kills, and turret destructions)--to describe the match information. This curated version removes redundant low-level frame details and purely cosmetic fields, reducing the JSON file size to approximately 160 kB per match while preserving all critical information. Throughout the remainder of this paper, we refer to this curated version as the \"raw\" dataset and use it as our baseline for evaluating the performance and effectiveness of our proposed compactor. This curated \"raw\" dataset, while not fully identical to the original Riot API response, closely mirrors realistic formats regularly utilized by professional esports analysts and serves as a practical baseline for our evaluations.  
The experiments focused on matches from two regions: North America (NA) using English (_en_US_) and Korea (KR) using Korean (_ko_KR_) language settings. Data were collected for two patch versions, 14.21 and 14.22, to evaluate the robustness of the LoL-MDC across different configurations. For each patch version, 10,000 curated matches were processed to assess the compactor's efficiency in reducing data size and token counts. Additionally, a subset of five anonymized matches was selected for qualitative evaluation, where GPT-4o responses were compared using raw and compacted JSON inputs. For the hyperparameters in the LoL-MDC, we set a 1 min interval for timeline view generation and selected the top five key events to summarize pivotal moments effectively.  
To evaluate tokenization efficiency, we utilized two open-source tokenizers: o200k_base[29] and cl100k_base[30]. The o200k_base tokenizer is employed by GPT-4o and GPT-4o-mini models, while the cl100k_base tokenizer is used by OpenAI models such as GPT-4, GPT-4-turbo, GPT-3.5-turbo, and embedding models. These tokenizers represent different encoding strategies and token capacities, enabling us to analyze how the LoL-MDC performs across diverse LLM architectures. File size reduction was measured in bytes, and token counts were calculated using the respective tokenizers to ensure consistency and comparability.  
Qualitative evaluations assessed the ability of compacted data to answer analytical questions by comparing LLM responses generated from raw JSON and compacted JSON files. To ensure consistency, all experiments used the standardized prompt as shown in Listing 1.  
```
1 ouareanexpertesportsanalystwithaspecializationinanalyzingcompetitivegamingdata.
2 Vourtaskistocevaluateandsummarzethegivenmatchdataandprovideaccuratetanddetailed\"insights.
3[JSON] {... }
4[Question] Whatwasthetotalgoldearnedbythewinningteam?
```  
**Listing 1**.: Standardized prompt for JSON based question-answering.