All for Goals: a Stylized Automated Analysis Framework in Football Matches

Automated analysis in football matches is meaningful for player and team evaluation. However, most related works ignore match style and team strength. In this paper, a novel stylized automated analysis framework termed All for Goals (AFG) is proposed for football matches, which considers match style and team strength to better quantify the relationship of all match states and player actions respectively with potential goals. AFG is composed of an automatic labeling module, a potential goal prediction module, and a state and player evaluation module. Specifically, in the automatic labeling module, relevant samples are given the same label to avoid manual labeling. In the potential goal prediction module, we introduce the Pretrain-Finetune paradigm. Based on labeled data, an average model learning to identify scoring difficulty is obtained in the first pre-training procedure, and tuned models learning specific styles are obtained in the second fine-tuning procedure. In the state and player evaluation module, the evaluation mechanisms of state, on-ball action, and off-ball running based on potential goal prediction result are designed for match review and tactics mining. Finally, we validate the rationality and validity of AFG on multiple tasks. On the goal prediction task, the models show high recall rates and remarkable difference in style. On real-time situation analysis, credit assignment for football events, and off-ball running analysis tasks, the evaluation mechanisms give the results consistent with football domain knowledge.