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：在体育数据分析中，球员追踪数据显著推动了球员评估任务的发展。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位置与假设性替代位置分布进行比较。通过建模美国国家橄榄球联盟（NFL）中单个持球球员的场内移动，我们展示了该方法的有效性。具体而言，我们基于两个组分构建了贝叶斯多层球员逐帧移动模型：步长（连续位置间的距离）与转角（连续步骤间的方向变化）。利用步长-转角模型，我们在比赛各帧中执行后验预测模拟，生成假设性持球球员步骤。这使观测到的球员移动能够通过美式橄榄球通用评估指标与模拟替代方案的分布进行比较。我们将该框架应用于2022赛季前九周的追踪数据，并基于假设性评估推导出新颖的球员表现指标。