In sports analytics, player tracking data have driven significant advancements in the task of player evaluation. We present a novel generative framework for evaluating the observed frame-by-frame player positioning against a distribution of hypothetical alternatives. We illustrate our approach by modeling the within-play movement of an individual ball carrier in the National Football League (NFL). Specifically, we develop Bayesian multilevel models for frame-level player movement based on two components: step length (distance between successive locations) and turn angle (change in direction between successive steps). Using the step-and-turn models, we perform posterior predictive simulation to generate hypothetical ball carrier steps at each frame during a play. This enables comparison of the observed player movement with a distribution of simulated alternatives using common valuation measures in American football. We apply our framework to tracking data from the first nine weeks of the 2022 NFL season and derive novel player performance metrics based on hypothetical evaluation.

翻译：在体育分析中，球员追踪数据推动了球员评估任务的重大进展。我们提出了一种新颖的生成式框架，用于将逐帧观测到的球员定位与假设替代方案的概率分布进行比较。我们通过建模美国国家橄榄球联盟（NFL）中单次进攻中持球者的场内运动来阐述该方法。具体而言，我们基于两个组成部分开发了帧级球员运动的贝叶斯多层次模型：步长（连续位置间的距离）和转向角（连续步骤间方向的变化）。利用步-转模型，我们进行后验预测模拟，以生成比赛中每一帧的假设持球者步态。这使得能够通过美式橄榄球中的常见价值评估指标，将观测到的球员运动与模拟替代方案分布进行比较。我们将该框架应用于2022年NFL赛季前九周的追踪数据，并基于假设评估推导出新颖的球员表现指标。