DIAMOND : An LLM -Driven Agent for Context-Aware Baseball Highlight Summarization (2025)

Abstract: Highlight summarization in baseball requires balancing statistical analysis with narrative coherence. Traditional approaches—such as Win Probability Added (WPA)-based ranking or computer vision-driven event detection—can identify scoring plays but often miss strategic depth, momentum shifts, and storyline progression. Manual curation remains the gold standard but is resource-intensive and not scalable.We introduce \textbf{DIAMOND} , an \textbf{LLM-driven agent for context-aware baseball highlight summarization} that integrates \textbf{structured sports analytics with natural language reasoning} . DIAMOND leverages sabermetric features—Win Expectancy, WPA, and Leverage Index—to quantify play importance, while an LLM module enhances selection based on contextual narrative value. This hybrid approach ensures both \textbf{quantitative rigor and qualitative richness} , surpassing the limitations of purely statistical or vision-based systems.Evaluated on five diverse Korean Baseball Organization League games, DIAMOND improves F1-score from 42.9% (WPA-only) to 84.8%, outperforming both commercial and statistical baselines. Though limited in scale, our results highlight the potential of modular, interpretable agent-based frameworks for event-level summarization in sports and beyond.

## introduction automating sports highlight generation presents a unique ai challenge: identifying impactful moments while preserving narrative coherence. in fast-paced sports like soccer or basketball, where crowd reactions and visual intensity often align with highlight-worthy moments, traditional ai methods-based on visual/audio cues-have been successful. in contrast, baseball presents a more episodic structure, where pivotal plays may be subtle and strategic rather than visually dramatic. traditional highlight generation methods rely on visual, audio, or social signals. visual-based models capture events like home runs or diving catches @xcite @xcite , but miss strategic plays such as defensive shifts or base-running decisions. audio-based methods use crowd noise or commentator emphasis @xcite , which may reflect excitement but not analytical significance. social engagement spikes @xcite often correlate with popular moments but not with game-critical context. these methods overlook the nuance of narrative flow and strategic buildup, particularly in sports like baseball. sabermetrics offers a structured, domainspecific lens for evaluating play impact through statistics like win expectancy (we), win probability added (wpa), and leverage index (li) @xcite @xcite . while useful for ranking events by outcome influence, purely statistical metrics fail to capture storytelling elements like momentum, context, or emotional resonance. this motivates approaches that go beyond quantification to capture qualitative game dynamics. we introduce diamond, an llm-driven agent for context-aware baseball highlight summarization. the name diamond evokes both the baseball field ("diamond") and the framework's goal of producing high-fidelity, interpretable highlights. figure 1 summarizes the challenges dia-mond addresses through a structured three-stage pipeline: preparation, decision, and reflection. in the preparation stage, we process structured playby-play logs and compute sabermetric scores. in the decision stage, we incorporate llm-based contextual scoring to assess narrative relevance. finally, in the reflection stage, we apply userdefined preferences and adjust rankings for narrative coherence. ## conclusion we introduced diamond, an llm-driven agent for context-aware baseball highlight summarization. by integrating structured sabermetric metrics with narrative-aware llm reasoning, diamond balances statistical rigor with storytelling coherence-without relying on multimodal inputs. despite being evaluated on a small but diverse set of kbo games, diamond outperforms both commercial and statistical baselines. ablation studies and expert evaluations highlight the effectiveness of its modular agent pipeline, with measurable gains from contextual scoring and user-guided refinement. future work will explore extensions to other sports and domains-for example, adapting the framework using expected goals (xg) in soccer or player efficiency rating (per) in basketball-as well as multimodal integration and lightweight llms for real-time applications.