Real-Soccer-Based Pass Receiver Choice Model for Soccer Agents

Abstract

In recent years, autonomous agents have been developed using statistical and probabilistic machine learnings together with deterministically optimized control. In this paper, a new decision making and motion generation method is proposed for adapting to uncertain environments. In the proposed method, we concentrate on passing in soccer, as a tactical group behavior, and understand how the optimization occurs in a group behavior from individual decision makings. In particular, we have quantified how people pass in plays by analyzing a video and tracking data of real soccer, and have constructed pass models with optimized parameters using logistic regression based on the analysis. As a result, our model predicted the next receiver with a high degree of accuracy by weighting positions of the players around the passer.