Football Player Position Prediction Using ML

Abstract

Precisely anticipating a player's position is critical to the success of team tactics and talent scouting in football, as every position requires a unique set of talents. This thesis uses an extensive dataset of 100,995 players and 14 important features to explore how machine learning could be able to simplify this challenging endeavor. Using nine different machine learning models (e.g., Random Forest, XGBoost, and LightGBM), the research carefully trains and assesses each model's prediction power. Under the direction of an exacting assessment methodology that includes accuracy, precision, recall, F1 Score, and AUC-ROC Curve, the study carefully adjusts hyperparameters to reach peak performance. With an astounding maximum accuracy of 90.42%, the study demonstrates the great potential of machine learning in football statistics. This research holds the potential to transform player assessment and tactical decision-making by revealing crucial insights into the interaction between players' locations and qualities. The ramifications go beyond the playing field; they provide a model for data-driven insights in a number of fields where it is essential to comprehend individual responsibilities within complex systems.