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
