Soccer player’s suitable playing position prediction using machine learning

Abstract

Accurately identifying the most suitable position for a football player is crucial for optimizing team performance and player development. This paper will investigate the potential of machine learning algorithms in predicting player positions based on various performance metrics. We propose a novel approach that will utilize Logistic Regression, K-Nearest Neighbors, Multi-Layer Perceptron, Random Forest Classifier, Support Vector Machine classifier, and Decision Tree to analyze a comprehensive dataset of player attributes, including physical stats, skill assessments, and positional data. The model will be evaluated on its ability to correctly predict the primary positions of players across different leagues and levels of competition. The results will demonstrate that our proposed approach achieves high accuracy which is in Logistic Regression and that is 75% Accuracy, in predicting a player's suitable playing position. Furthermore, we analyze the feature importance scores to gain insights into the key attributes that are most influential in determining player positions