PEP：衡量预期得分阻止效果的擒抱价值指标
PEP: a tackle value measuring the prevention of expected points
AI总结：
针对美式橄榄球传统擒抱评估忽略情境与重要性的问题，提出基于阻止预期得分的PEP指标，结合高分辨率追踪数据与随机森林、混合效应模型量化擒抱价值，助力防守表现分析。
摘要：
美式橄榄球中传统的擒抱评估通常仅考虑擒抱完成次数，未充分考量其比赛情境与对比赛的重要性。为改进这一现状，我们提出了一种量化擒抱价值的指标，以被阻止的预期得分（PEP）为衡量维度。具体而言，我们将擒抱发生后实际的进攻码线，与假设擒抱未成功的虚拟情境下预测的码线进行对比。为此，我们使用了可捕捉球员位置与速度的高分辨率追踪数据，并采用随机森林算法来处理码线预测中的不确定性与多模态特性。此外，我们通过为随机森林的每棵决策树预测结果分配预期得分值，体现不同擒抱的重要性差异。最后，为将擒抱价值与球员的擒抱能力相关联，我们对PEP值拟合了合适的混合效应模型。该方法有助于深化对美式橄榄球防守表现的理解，为教练与分析师提供有价值的参考。
英文摘要：
Traditional assessments of tackling in American Football often only consider the number of tackles made, without adequately accounting for their context and importance for the game. Aiming for improvement, we develop a metric that quantifies the value of a tackle in terms of the prevented expected points (PEP). Specifically, we compare the real end-of-play yard line of tackles with the predicted yard line given the hypothetical situation that the tackle had been missed. For this, we use high-resolution tracking data, that capture the position and velocity of players, and a random forest to account for uncertainty and multi-modality in yard-line prediction. Moreover, we acknowledge the difference in the importance of tackles by assigning an expected points value to each individual tree prediction of the random forest. Finally, to relate the value of tackles to a player's ability to tackle, we fit a suitable mixed-effect model to the PEP values. Our approach contributes to a deeper understanding of defensive performances in American football and offers valuable insights for coaches and analysts.
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。
