Goalstats - An application for statistics and predicitve analytics in football using Xg Algorithm in AI and Machine learning

Data analytics in sports and specifically in footballhas tremendous potential. At present, an increasing numberof teams in top leagues are implementing this in the hopesthat this strategy can lead to improved performances on thepitch. This paper outlines a web application that serves toprovide detailed statistics on almost all football games withinternational coverage, along with certain functionalities makinguse of machine learning. Using this application, users can makepredictions for the market values of players with reasonableaccuracy. Other functionalities include a player similaritymodule, where users can check how similar players are to oneanother based on their abilities, position on the pitch, and theirpotential for improvement. Such a metric can be useful forfinding replacements for players in teams. Finally, an analysisof expected goals is provided based on events within selectedgames. The data was collected through scraping and data-setsavailable online while the application software was created usingthe ReactJS and Flask frameworks.