Data_Sheet_1_Process Mining of Football Event Data: A Novel Approach for Tactical Insights Into the Game.docx

• Main Authors: Pavlina Kröckel, Freimut Bodendorf
• Format: Dataset
• Language: unknown
• Published: 2020
• Subjects: Artificial Intelligence and Image Processing; Knowledge Representation and Machine Learning; Applied Statistics; Computational Linguistics; football; soccer; process mining; sports analytics; tactics; Iceland
• Online Access: https://doi.org/10.3389/frai.2020.00047.s001; https://figshare.com/articles/dataset/Data_Sheet_1_Process_Mining_of_Football_Event_Data_A_Novel_Approach_for_Tactical_Insights_Into_the_Game_docx/12651071

The paper explores process mining and its usefulness for analyzing football event data. We work with professional event data provided by OPTA Sports from the European Championship in 2016. We analyze one game of a favorite team (England) against an underdog team (Iceland). The success of the underdog teams in the Euro 2016 was remarkable, and it is what made the event special. For this reason, it is interesting to compare the performance of a favorite and an underdog team by applying process mining. The goal is to show the options that these types of algorithms and visual analytics offer for the interpretation of event data in football and discuss how the gained insights can support decision makers not only in pre- and post-match analysis but also during live games as well. We show process mining techniques which can be used to gain team or individual player insights by considering the types of actions, the sequence of actions, and the order of player involvement in each sequence. Finally, we also demonstrate the detection of typical or unusual behavior by trace and sequence clustering.