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