Forecasting Results of Sport Events Through Deep Learning

The importance of competitive sport events such as the World Cup and the World Baseball Classic for a majority of people can be easily found through the heated discussions in newspapers and other types of media such as the Internet while the fad hits. They are also highly discussed topics. Many people are even one-day fans with the same expectations; that is, they want their team to win. It is, however, very difficult to determine which team will stand out among the many. In this study, records and data from the many contests that the National Basketball Association (NBA), which also deals with competitive sports, has held will be analyzed and discussed in order to forecast results of games. The deep learning approach will be adopted and convolutional neural networks and data from 4147 games over the past 3 years will be used for analysis and to facilitate training on and forecasts done applying the model. Finally, forecasting results will be discussed. In previous studies, convolutional neural networks were more frequently applied to identifying images or objects. Therefore, with the current study, the hope is to combine deep learning in the forecast of event results and that the approach helps add to the accuracy of forecast results compared to other classifiers.