Expected goals of a football match determine whether a team have won or lost. When considering the expected goal results, a team may appear to have lost the game but actually win it, and vice versa. The expected goal is the amount of goals a team should have scored based on the information available for that particular game. Numerous machine learning algorithms are employed to measure the effectiveness of shots in football. In this paper, we develop a gradient Boosting model to evaluate the scoring opportunities using event data collected from live football matches. This method can be used to show the players who are most likely to score at any given time throughout the game as well as where on the field they are most likely to do so. Experimental results demonstrate that they recognise and assess significant match opportunities and analyze the players in a football match depending upon their performance using Expected goals(xG) as an evaluation metric.