Fractional Tackles：利用球员追踪数据评估美式橄榄球进攻回合内的擒抱表现
Fractional Tackles: Leveraging Player Tracking Data for Within-Play Tackling Evaluation in American Football
AI总结：
本文提出 fractional tackles 框架，利用球员追踪数据识别接触窗口并连续、客观地量化防守球员的擒抱贡献，弥补传统统计对防守线锋等球员衡量不足的问题。
摘要：
擒抱是美式橄榄球中的一项基本防守动作，其主要目的是阻止持球球员的向前推进。然而，当前的擒抱指标是人工记录的结果，由于其离散性和主观性，本身存在缺陷。我们利用球员追踪数据，提出了一种以连续、客观方式评估擒抱贡献的新颖框架。该方法首先在一次进攻回合中识别防守球员何时处于持球球员的“接触窗口”内，随后为每个窗口及参与其中的球员分配价值。这使我们能够设计出一种名为 fractional tackles（部分擒抱）的新指标，用于认可防守球员在阻止持球球员向端区推进方面的贡献。我们证明，fractional tackles 克服了擒抱次数和助攻等传统指标的不足：它提供了更大的差异性，并能为防守线锋等缺乏记录统计数据的球员提供可衡量的信息。我们认为，这项贡献是美式橄榄球防守表现衡量方面的重要进步，也清楚展示了球员追踪数据的能力。
英文摘要：
Tackling is a fundamental defensive move in American football, with the main purpose of stopping the forward motion of the ball-carrier. However, current tackling metrics are manually recorded outcomes that are inherently flawed due to their discrete and subjective nature. Using player tracking data, we present a novel framework for assessing tackling contribution in a continuous and objective manner. Our approach first identifies when a defender is in a ``contact window'' of the ball-carrier during a play, before assigning value to each window and the players involved. This enables us to devise a new metric called fractional tackles, which credits defenders for halting the ball-carrier's forward motion toward the end zone. We demonstrate that fractional tackles overcome the shortcomings of traditional metrics such as tackles and assists, by providing greater variation and measurable information for players lacking recorded statistics like defensive linemen. We view our contribution as a significant step forward in measuring defensive performance in American football and a clear demonstration of the capabilities of player tracking data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
