Prediction of pitch type and location in baseball using ensemble model of deep neural networks

In the past decade, many data mining researches have been conducted on the sports field. In particular, baseball has become an important subject of data mining due to the wide availability of massive data from games. Many researchers have conducted their studies to predict pitch types, i.e., fastball, cutter, sinker, slider, curveball, changeup, knuckleball, or part of them. In this research, we also develop a system that makes predictions related to pitches in baseball. The major difference between our research and the previous researches is that our system is to predict pitch types and pitch locations at the same time. Pitch location is the place where the pitched ball arrives among the imaginary grids drawn in front of the catcher. Another difference is the number of classes to predict. In the previous researches for predicting pitch types, the number of classes to predict was 2∼7. However, in our research, since we also predict pitch locations, the number of classes to predict is 34. We build our prediction system using ensemble model of deep neural networks. We describe in detail the process of building our prediction system while avoiding overfitting. In addition, the performances of our prediction system in various game situations, such as loss/draw/win, count and baserunners situation, are presented.