AI based Goalkeeper for Penalty Shot Prediction using RNN and Pattern Matching Algorithm

Penalty kicks and shootouts in football are very high-pressure situations for both the goalkeeper and the penalty taker. This paper aims to introduce the concept of using an AI goalkeeper to simulate and predict penalty kicks. The main objective is to use an AI goalkeeper application to teach players to have randomness in their penalty shots. This application can also be used to predict the direction of the next penalty kick by a penalty taker, which can enable real-life goalkeepers to train themselves to be ready to face it on match day. The paper also showcases different direction picking techniques and how the AI goalkeeper developed by us using a recurrent neural network (RNN) and a pattern matching algorithm reacts. This is done to demonstrate why randomness in the shooting direction history of a player is important in penalty kicks as well as shootouts. To showcase the importance of the directional foresight of penalty shots and the proficiency of the AI goalkeeper in predicting the shot direction, a comparative study is done by taking a professional player's penalty shot direction history and inputting it to the AI goalkeeper. Finally, the paper provides applications for using an AI goalkeeper and concludes the research findings. This article serves as an initial theoretical base for the use of AI in sports. With much improvement in the AI goalkeeper program, it is very much possible to use it for the objectives mentioned in the paper.