We develop a new family of marked point processes by focusing the characteristic properties of marked Hawkes processes exclusively to the space of marks, providing the freedom to specify a different model for the occurrence times. This is possible through the decomposition of the joint distribution of marks and times that allows to separately specify the conditional distribution of marks given the filtration of the process and the current time. We develop a Bayesian framework for the inference and prediction from this family of marked point processes that can naturally accommodate process and point-specific covariate information to drive cross-excitations, offering wide flexibility and applicability in the modelling of real-world processes. The framework is used here for the modelling of in-game event sequences from association football, resulting not only in inferences about previously unquantified characteristics of game dynamics and extraction of event-specific team abilities, but also in predictions for the occurrence of events of interest, such as goals, corners or fouls in a specified interval of time.
Automated Detection of Match Phases in Football from Spatio-Temporal Tracking Data Using Graph Neural Networks
Institute of Statistics, Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院统计研究所）；Karlsruher Sport-Club（卡尔斯鲁厄体育俱乐部）；Broad Institute of MIT & Harvard（麻省理工学院与哈佛大学生物医学 Broad 研究所）；German Football Association (DFB)（德国足球协会）；Chair for Sports Analytics, Saarland University（萨尔兰大学体育分析讲席）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）
Modeling Events and Interactions through Temporal Processes -- A Survey
University of Calabria（卡拉布里亚大学）；University G. d’Annunzio of Chieti-Pescara（基耶蒂-佩斯卡拉加布里埃尔·邓南遮大学）；University of Pisa（比萨大学）；Faculty of Economics - University of Porto（波尔图大学经济学院）；Scuola Normale Superiore of Pisa（比萨高等师范学校）
