Comprehensive Prediction Model for Player Selection in FIFA Manager Mode

Abstract

Game is one of the most entertaining shows for today’s all generation peoples, particularly Football in most part of countries of the world. Football as a sport is only growing more and more popular every day. It is currently the world’s most-watched sport and has the highest viewership audience. As a result, a whole industry has arisen around this sport with one important part of it being FIFA. The amount of budget allocated and the number of persons involved in a Football game directly or indirectly can affect the financial budget of a person to a federation's finance. In such cases, player selection for a finalist from the federation is the most crucial task. Every year different approaches were investigated for player selections, but none of them was regarded as the best approach for team selection. Thus, there is a need for a standard approach for finding out the perfect players for their teams with the exact qualities that they demand. In response, we have developed a machine learning model that predicts players who could replace a current existing player in a team. Along with that, we have also incorporated Data Analytics that helps us decide which factors would be more important than others. The proposed prediction model is implemented and the results of our machine learning (SAGA-ML) tool are applied to Electronics Arts’ FIFA Soccer game. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.