Date of Award
Spring 2021
Degree Name
Bachelor of Science (BS)
Advisor(s)
Yilu Zhou
Abstract
This project analyzes the possibility of predicting professional Counter Strike: Global Offensive (CS:GO) matches using machine learning. Data points were pulled from HLTV on over 700 competitive players and over 1,900 teams spanning over 100,000 games and put into/cleaned in excel. Additional data points were added, and the data was then analyzed on an individual and a team basis and put through a data miner to find relationships in winning versus losing outcomes. The results show that it is possible to accurately predict the winning team 68-73% of the time. The results also highlight key attributes that are most instrumental in predicting outcome.
Recommended Citation
Forgey, Henry, "Predicting Game Outcome of Counter Strike with Machine Learning: A Study of Global Offensive Competitive Matches" (2021). Gabelli School of Business Honors Thesis Collection. 53.
https://research.library.fordham.edu/gabelli_thesis/53
