The National Basketball Association (NBA) is the most popular basketball league in the world. The world-wide mighty high popularity to the league leads to large amount of interesting and challenging research problems. Among them, predicting the outcome of an upcoming NBA match between two specific teams according to their historical data is especially attractive. With rapid development of machine learning techniques, it opens the door to examine the correlation between statistical data and outcome of matches. However, existing methods typically make predictions before game starts. In-game prediction, or real-time prediction, has not yet been sufficiently studied. During a match, data are cumulatively generated, and with the accumulation, data become more comprehensive and potentially embrace more predictive power, so that prediction accuracy may dynamically increase with a match goes on. In this study, I design game-level and player-level features based on realtime data of NBA matches and apply a machine learning model to investigate the possibility and characteristics of using real-time prediction in NBA matches.