Real-time prediction to support decision-making in soccer

Abstract

Data analysis in sports has been developing for many years. However, to date, a system that provides tactical prediction in real time and promotes ideas for increasing the chance of winning has not been reported in the literature. Especially, in soccer, components of plays and games are more complicated than in other sports. This study proposes a method to predict the course of a game and create a strategy for the second half. First, we summarize other studies and propose our method. Then, data are collected using the proposed system. From past games, games to similar to a target game are extracted depending on data from their first half. Next, similar games are classified by features depending on data of their second half. Finally, a target game is predicted and tactical ideas are derived. The practicability of the method is demonstrated through experiments. However, further improvements such as increasing the number of past games and types of data are still required.