用大模型预测棒球每局进展，一次建模搞定多环节预测。
Neural Sabermetrics with World Model: Play-by-play Predictive Modeling with Large Language Model
- 把棒球比赛看作连续事件序列，用大模型持续训练
- 单次击球后下一球预测准确率64%，击球选择预测准确率78%
- 适合想做体育动态建模或生成的科研与从业者
传统棒球统计学通过压缩历史数据生成简洁指标，虽利于评估与回溯分析，却无法构建逐球推进的游戏生成模型，现有方法多限于单步预测或事后分析。本文提出基于大语言模型的棒球世界模型——神经棒球统计学（Neural Sabermetrics with World Model），将棒球比赛视为长序列自回归事件，对超过十年的美国职棒大联盟（MLB）追踪数据（包含七百万次投球序列及约三亿个词元）进行持续预训练。所获模型可在统一框架内预测游戏演进的多个方面。在常规赛与季后赛数据上均进行评估，结果表明，尽管仅使用单一骨干模型，其表现仍优于既有神经基线：在一次击球中，能正确预测约64%的下一球；对击球员是否挥棒的判断准确率达78%。这表明大语言模型可作为体育运动的有效世界模型。
原文摘要 · Abstract (English)
Classical sabermetrics has profoundly shaped baseball analytics by summarizing long histories of play into compact statistics. While these metrics are invaluable for valuation and retrospective analysis, they do not define a generative model of how baseball games unfold pitch by pitch, leaving most existing approaches limited to single-step prediction or post-hoc analysis. In this work, we present Neural Sabermetrics with World Model, a Large Language Model (LLM) based play-by-play world model for baseball. We cast baseball games as long auto-regressive sequences of events and continuously pretrain a single LLM on more than ten years of Major League Baseball (MLB) tracking data, comprising over seven million pitch sequences and approximately three billion tokens. The resulting model is capable of predicting multiple aspects of game evolution within a unified framework. We evaluate our model on both in-distribution regular-season data and out-of-distribution postseason games and compare against strong neural baselines from prior work. Despite using a single backbone model, our approach outperforms the performance of existing baselines, (1) correctly predicting approximately 64% of next pitches within a plate appearance and (2) 78% of batter swing decisions, suggesting that LLMs can serve as effective world models for sports.
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