Prediction of Football Player Performance Using Machine Learning Algorithm

Abstract we delve into the popular subject of predicting soccer match outcomes, particularly focused on the local team's games, which often spark enthusiastic discussions among male audiences. The endeavor to model football data has gained traction in recent years, prompting the emergence of diverse methodologies aimed at deciphering the factors influencing a team's victory or defeat, or even predicting match scores. Central to our study is leveraging machine learning and data mining techniques to forecast match results by analyzing historical match data. Employing tools such as WEKA, we identify key features contributing to match outcomes. Through the application of various classifiers including logistic regression, SVM, and Bayesian networks, we rigorously test and refine our predictive models. Ultimately, we propose the most influential features and outline strategies for computing new parameters derived from these features, thereby enhancing the accuracy of our predictions.