基于多元霍克斯过程的带时间戳足球网络中的信用归因
Credit Attribution in Timestamped Soccer Networks Using Multivariate Hawkes Processes
AI总结：
本研究针对传统足球球员统计的局限性，提出用多元霍克斯过程建模传球射门序列，构建预期进球参与度指标，以更全面衡量球员进攻参与度。
摘要：
足球中传统的两项主要球员统计数据是进球数和助攻数，当球员的传球直接导致队友进球时，该球员会被记一次助攻。仅将功劳归因于进球前完成最后一次传球的球员，无法考虑到进球筹备阶段多名球员的贡献，因此传统统计数据仅能提供球员参与情况的有限视角。本文使用多元霍克斯过程（Multivariate Hawkes Processes, MHPs）对带时间戳的传球和射门序列进行建模，这些序列构成连续时间网络。MHPs 提供了一种分支机制，其中事件（传球或射门）可作为前序事件的子事件生成，这使我们能够量化每次传球对后续传球或射门的贡献。该方法将功劳分配给进攻序列中的多次传球，适用于多种场景。基于此框架，我们提出了预期进球参与度（expected goal involvements, xGI），这一指标基于球员的传球衡量其在创造进球得分机会中的参与程度。该指标能够识别出即使未完成最后一次传球但在创造进球得分机会中发挥关键作用的球员，提供了对进攻参与度和球员表现更全面的衡量。
英文摘要：
The two main traditional player statistics used in soccer are goals scored and assists, with an assist credited when a player's pass immediately leads to a teammate scoring. Attributing credit solely to the player who makes the final pass before a goal fails to account for the contributions of multiple players in the buildup to the goal. Consequently, traditional statistics provide only a limited view of player involvement. In this paper, we use multivariate Hawkes processes (MHPs) to model timestamped sequences of passes and shots, which form continuous-time networks. MHPs provide a branching mechanism where an event (a pass or shot) can be generated as a child of a previous event, which allows us to quantify each pass's contribution to a subsequent pass or shot. This approach distributes credit across multiple passes within an attacking sequence, making it applicable across a wide range of situations. Building on this framework, we propose expected goal involvements (xGI), a metric that measures a player's involvement in creating goal scoring opportunities based on their passes. This metric identifies players who play key roles in creating goal scoring opportunities even if they do not make the final pass, offering a more comprehensive measure of attacking involvement and player performance.
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。
