Information completion using a deep generative model for counterfactual evaluation of spatio-temporal event data in group sports

Abstract

With the development of measurement technology, people in various fields analyze multi-agent spatio-temporal data. However, since the analysis is limited to what occurred and there is a lack of information in the data, there are challenges in utilizing the results of data analysis for future decision-making and in finding solutions to data uncertainty. Hence, counterfactual and information completion play an essential role, but only some examples of applying these methods to the data exist. This paper proposes methods to evaluate behavior by considering counterfactuals and to complement information for the data. In particular, we do this for open soccer data that includes information about a more significant number of agents. The former proposes a method to evaluate team defenses by considering counterfactuals with a mathematical model that is easy for people to understand. The latter proposes a deep generative model that can complement information about players' velocities lacking in such data while considering features related to agents' interactions. We expect this research will enable more people to analyze and support players and coaches in decision-making.