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Soccer Simulation 2D (SS2D) is a simulation of a real soccer game in twodimensions. In soccer, passing behavior is an essential action for keeping theball in possession of our team and creating goal opportunities. Similarly, forSS2D, predicting the passing behaviors of both opponents and our teammateshelps manage resources and score more goals. Therefore, in this research, wehave tried to address the modeling of passing behavior of soccer 2D playersusing Deep Neural Networks (DNN) and Random Forest (RF). We propose an embeddeddata extraction module that can record the decision-making of agents in anonline format. Afterward, we apply four data sorting techniques for trainingdata preparation. After, we evaluate the trained models' performance playingagainst 6 top teams of RoboCup 2019 that have distinctive playing strategies.Finally, we examine the importance of different feature groups on theprediction of a passing strategy. All results in each step of this work proveour suggested methodology's effectiveness and improve the performance of thepass prediction in Soccer Simulation 2D games ranging from 5\% (e.g., playingagainst the same team) to 10\% (e.g., playing against Robocup top teams).

Engineering Features to Improve Pass Prediction in Soccer Simulation 2D Games