In the current world, sports produce considerable statistical information about each player, team, games, and seasons. Traditional sports science believed science to be owned by experts, coaches, team managers, and analyzers. However, sports organizations have recently realized the abundant science available in their data and sought to take advantage of that science through the use of data mining techniques. Sports data mining assists coaches and managers in result prediction, player performance assessment, player injury prediction, sports talent Identification and game strategy evaluation. Predicting the results of sports matches is interesting to many, from fans to punters. It is also interesting as a research problem, in part due to its difficulty: the result of a sports match is dependent on many factors, such as the morale of a team (or a player), skills, coaching strategy, etc. So even for experts, it is very hard to predict the exact results of individual matches. The present study reviews previous research on data mining systems to predict sports results and evaluates the advantages and disadvantages of each system. Manuscript profile