Forecasting Methods and Computational Complexity for the Sport Result Prediction

Competition in gaming is one of the most widely audience watching team game in the ecosphere. Predicting the conclusion of a contest has developed so simple because to advancements in technology. We are utilising machine learning methods such as supervised learning to envisage the winner of a One Day International (ODI) cricket match. In order to train the models, we use each player's own career statistics as well as squad recitals such as flickering and speeding performances. The ability of players and many parameters, on the other hand, play an important role in determining the ultimate outcome of a match. As a result, we're employing supervised learning algorithms to forecast the match's outcome. It also aids the team's coaches in learning and analysing where the team's performance is lacking, so that the coaches may discover a solution to increase their team's strength. So, in this study, we use four different sorts of machine learning algorithms and compare them to each other to see which one is the most accurate and produces the greatest results.