Estimation of football players' skills is one of the key tasks in sports
analytics. This paper introduces multiple extensions to a widely used model,
expected possession value (EPV), to address some key challenges such as
selection problem. First, we assign greater weights to events occurring
immediately prior to the shot rather than those preceding them (decay effect).
Second, our model incorporates possession risk more accurately by considering
the decay effect and effective playing time. Third, we integrate the assessment
of individual player ability to win aerial and ground duels. Using the extended
EPV model, we predict this metric for various football players for the upcoming
season, particularly taking into account the strength of their opponents.

估计足球运动员技能是体育分析中的一个关键任务。本文介绍了对广泛使用的期望控球价值模型进行多次扩展，以解决选择问题等关键挑战。首先，我们赋予射门前紧密发生的事件比其之前发生的事件更大的权重（衰减效应）。其次，我们的模型通过考虑衰减效应和有效比赛时间更准确地融入控球风险评估。第三，我们整合了个体球员在空中和地面的交锋中胜利的能力评估。使用扩展的期望控球价值模型，我们预测了即将到来赛季各种足球运动员的这一度量，特别考虑了对手的实力。

足球球员技能估计的控制与对抗动作的预期控制价值

Expected Possession Value of Control and Duel Actions for Soccer  Player's Skills Estimation

Many NLP tasks can be regarded as a selection problem from a set of options,
such as classification tasks, multi-choice question answering, etc. Textual
entailment (TE) has been shown as the state-of-the-art (SOTA) approach to
dealing with those selection problems. TE treats input texts as premises (P),
options as hypotheses (H), then handles the selection problem by modeling (P,
H) pairwise. Two limitations: first, the pairwise modeling is unaware of other
options, which is less intuitive since humans often determine the best options
by comparing competing candidates; second, the inference process of pairwise TE
is time-consuming, especially when the option space is large. To deal with the
two issues, this work first proposes a contextualized TE model (Context-TE) by
appending other k options as the context of the current (P, H) modeling.
Context-TE is able to learn more reliable decision for the H since it considers
various context. Second, we speed up Context-TE by coming up with Parallel-TE,
which learns the decisions of multiple options simultaneously. Parallel-TE
significantly improves the inference speed while keeping comparable performance
with Context-TE. Our methods are evaluated on three tasks (ultra-fine entity
typing, intent detection and multi-choice QA) that are typical selection
problems with different sizes of options. Experiments show our models set new
SOTA performance; particularly, Parallel-TE is faster than the pairwise TE by k
times in inference. Our code is publicly available at
this https URL.

本文提出了一种基于文本蕴涵的上下文化选择模型 Context-TE，以及一种能够快速进行推理的并行选择模型 Parallel-TE，这两种模型在三项常见选择问题任务中实现了 state-of-the-art 的性能表现，其中 Parallel-TE 在推理过程中将 pairwise TE 的时间复杂度降低了 k 倍。