Growing up, my best friend and I liked to play video games. While we weren’t very good, we were amazed by the growth of eSports and the size of the live events that were happening. Unfortunately, as time went on and we went to college, we fell out of touch with the eSports and gaming communities. It is wild what we missed! It really is incredible how much investment the eSports industry has received, and it has developed from a very niche audience to a well-produced broadcast/event that appeals to many people.
The 2020 Coronavirus outbreak and subsequent lockdown really made me appreciate eSports’ role in the sports landscape. While physical sports were not able to participate in any competition, eSports was able to do relatively well online. As such, I filled some of the lockdown time getting back into eSports (most notably CS:GO). Since the usual sports were not creating any new data, I decided I would try my hand at some eSports analytics.
I did all scraping (thank you HLTV) in the R programming language. All of the data is stored in a local PostgreSQL database. I chose R for the scraping and analysis, since its DBI and RPostgres packages work seamlessly with the local database.