Performance prediction in major league baseball by long short-term memory networks

Player performance prediction is a serious problem in every sport since it brings valuable future information for managers to make important decisions. In baseball industries, there already existed variable systems and many types of researches that attempt provide accurate predictions help domain users. However, lack studies about the predicting method or based on deep learning. Deep learning models had proven be greatest solutions different fields nowadays, so we believe they could tried applied baseball. Hence, abilities are set our research this paper. As beginning, select numbers home runs as target because one most critical indexes understand power talent hitters. Moreover, use sequential model Long Short-Term Memory main solve run Major League Baseball. We compare models’ ability with several machine widely used projection system, sZymborski Projection System. Our results show has better than others more exact predictions. conclude feasible way problems bring fit users’ needs.