An Intelligent Decision Support System for Bid Prediction of Undervalued Football Players

Abstract

The process of selecting football team players will determines a team's performance. An effective team is made up of a successful group of individual talented players. In general, a football team player selection is a decision made by the club based on the best available information. Club managers and scouts travel to different countries to watch matches and hire the best talent that can help their club to perform better. But for the lower leagues, it becomes difficult to hire the same talents because of strict budget. Here we devise a method so that we can leverage the undervalued players to get selected by the clubs. Clearly the benefit will be in two fold. First, the smaller clubs can get better players at an affordable cost. Second, the bigger clubs can get same performance players at a lower price helping them in cost cutting. We employ novelty detection methods to find out the undervalued players from our data and investigate our method by using five machine learning models. For performance evaluation, the five machine learning models used are support vector machine, Random Forest, Decision Tree, Linear Regression and XGBoost. Here XGboost performed best both for 10 fold cross-validation and external testing with a RMSE of 0.0122 and 0.0107 respectively.