CS: GO Analytics Package
Research Abstract: The use of analytics in professional sports is widespread and rapidly increasing. Similarly, there is a need for analytics in the emerging area of esports, or professional video gaming. Counter-Strike: Global Offensive, also known as CS: GO, is one of the most popular esports with over forty million copies sold, yet it has lacking analytics. This impedes simple and efficient evaluation of competitive CS: GO matches, player performance, and team performance, which is critical to teams, bettors, media, and fans. To this end, we introduce an analytics package consisting of (1) generalized functions to allow for the efficient filtering and aggregation of data; and (2) specialized functions to allow for the efficient calculation of CS: GO match statistics.
Mentorship: I researched under Dr. Claudio Silva, Professor of Computer Science and Engineering at NYU Tandon and Professor of Data Science at the NYU Center for Data Science, and Dr. Peter Xenopoulos at the Visualization, Imaging, and Data Analytics Center at New York University. My analytics package was published as part of the awpy library, a Python library to parse, analyze and visualize CS: GO data that is on the Python Package Index (PyPI).
Recognition: My research was accepted to the Johns Hopkins Institute for Data Intensive Engineering and Science (IDIES) and I presented it at the 2021 Symposium.
awpy Library: https://pypi.org/project/awpy/
IDIES Symposium Website: https://www.idies.jhu.edu/news-events/events/idies-annual-symposium/