Predicting the outcome of future football games using machine learning algorithms

Sports predictions has gained popularity as a topic of study thanks to the development of sports analytics and the accessibility of extensive and sophisticated match data. In this paper, a machine learning-based (ML) strategy for forecasting football match results is presented. In the past, the number of goals scored by each team in a football (FB) game served as a benchmark for assessing a team's performance and projecting future outcomes to forecast the results of football games, such as win, draw, or defeat. However, there are significant discrepancies between a team's performance and the number of goals scored or conceded throughout many games because the number of goals scored during a match contains a significant random element.The goal of this study is to create an accurate and dependable prediction model that will help fans, analysts, and bet enthusiasts make wise selections. The selection of features, treatment of data that is imbalanced and the generalization of models constitute significant issues that are addressed in this paper. The system learns patterns from previous encounters and player attributes to develop predictions for upcoming games. Various machine learning algorithms, including random forests (RF), support vector machines (SVM), and regression, are experimented with. The main goal of this research is to investigate various Machine Learning techniques to forecast football game results utilizing in-game match events and individual and team statistics off the field. As football remains a globally popular sport, the integration of machine learning for match prediction offers an exciting avenue for enthusiasts to enhance their understanding and enjoyment of the game.