Positioning soccer players for success: a data-driven machine learning approach

Determining a player's proper position in football is critical for maximizing their impact on the field. In this study, we propose a scientific and analytical approach to address this issue using machine learning models. We use the FIFA dataset to identify the correct positions for players and show that the logistic regression model provides the most accurate predictions, with an average accuracy of 99.84\% on test data across the all positions. To further refine player positioning, we use the Recursive Feature Elimination (RFE) method to identify the most important features associated with each position. The top five features identified through RFE are used to evaluate players' suitability for their correct positions and we illustrate that the average Mean Squared Error (MSE) is 1.166 on a scale of 100, indicating high accuracy in predicting their suitability scores. Overall, our results suggest that the logistic regression model is an effective tool for accurately determining player positions, and that the selected features can be used to evaluate players' suitability for a given position with high accuracy. Our approach provides a data-driven solution to help teams make better decisions in player selection and positioning, potentially leading to improved team performance and success.