  Soccer Simulation 2D (SS2D) is a simulation of a real soccer game in two
dimensions. In soccer, passing behavior is an essential action for keeping the
ball in possession of our team and creating goal opportunities. Similarly, for
SS2D, predicting the passing behaviors of both opponents and our teammates
helps manage resources and score more goals. Therefore, in this research, we
have tried to address the modeling of passing behavior of soccer 2D players
using Deep Neural Networks (DNN) and Random Forest (RF). We propose an embedded
data extraction module that can record the decision-making of agents in an
online format. Afterward, we apply four data sorting techniques for training
data preparation. After, we evaluate the trained models' performance playing
against 6 top teams of RoboCup 2019 that have distinctive playing strategies.
Finally, we examine the importance of different feature groups on the
prediction of a passing strategy. All results in each step of this work prove
our suggested methodology's effectiveness and improve the performance of the
pass prediction in Soccer Simulation 2D games ranging from 5\% (e.g., playing
against the same team) to 10\% (e.g., playing against Robocup top teams).
