Basketball Statistical Analyzer: Live Online

Student Classification

Ismail Conze, 4th Year, Computer Science Kalyn Matthews, 4th Year, Computer Science

Faculty Mentor

Dr. Xiaohong Yuan, Department of Computer Science, North Carolina Agricultural and Technical State University


Department of Computer Science

Document Type


Publication Date

Spring 2023


Our Live onLine statistical analyzer will serve as a moderately equipped NBA game predictor using reliable basketball stats as well as potent history, to heighten the fantasy league experience. The world of betting as we know it has relied heavily on the ability to make concise hypotheses of favorable teams/opponents. To which partner(s) and I sought to fulfill through the increased use of machine learning and selective features. With this web application we seek to implement an analysis of basketball wins and losses on a per team, per game basis using a feature set. In doing so we anticipate to empower user interaction and capabilities as it pertains to the overall user experience. We will enable user access to reflect on team/game succession and progression starting from the year 1990 to present. One can refer to the status of wins and losses based on data derived from machine learning mechanics provided. The inclusion of the specified feature/datasets is to maximize the fan experience as it pertains to past team/game statistics and future hypotheses. Support Vector Machines adequately predict contingent game results.

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