Artificial Intelligence-Based Models for Prediction of Major League Baseball Matches Outcome – A Systematic Mapping

In the present time, MLB - Major League Baseball is a professional biggest sporting event at international level. And the researchers are very much keen about the prediction of results in this particular game. Many have tried envisaging and forecasting the outcome of these matches on the basis of trend and scenario. But the predicting outcome accuracy levels of these matches are not up to the mark, it ranges between 50 to 60 percent only. Therefore, nowadays to improve this accuracy, various artificial-intelligence based methods are being used. The purpose of this research is to analyze and compare different machine learning models used by various authors over the years. It will assess these models on dimensions like Flexibility, Complexity and Interpretability. With the help of these comparisons the researcher will also suggest how improvements can be made in these models so that higher level of accuracy can be achieved in predicting the results of the game.