This thesis presents the design of sports match predictions with various approaches, including linear models based on different evaluation metrics of players' performance and neural networks. Our main goal is to incorporate this prediction model into the modern conversational AI and help the bot to make more interesting and human-like conversations. Therefore, our models have high interpretability to convey information about various aspects of a sports match. While our model can talk about players' performance of past matches, predictions of further matches and players' impact, it can still achieve an accuracy of 68.7% on the NBA and 67.5% on the MLB match predictions. Moreover, our model has a high generality and applicable to new sports. Hence, it will be easy for further development and expansion after being incorporated into the conversation system.
Computer Science / Emory University
BS / Fall 2020
Jinho D. Choi, Computer Science and QTM, Emory University (Chair)
Michelangelo Grigni, Computer Science, Emory University
Yuanzhe Xi, Mathematics, Emory University