Background

As video games have evolved throughout the years, so have the ways in which they were played. Where video games used to be for children to enjoy with their friends to pass the time in their homes, they are now played across the globe in fierce competitions, with millions of viewers watching in live stadiums as well as in their homes, accompanied by life changing prize pools. In this report we will be looking at one of the most played games in the world; Counter-Strike: Global Offensive, or CS:GO, and how much money is made from competing in these Counter Strike tournaments.

Using the below code we can see how much money has been collected won across all league sanction tournaments, called “Majors”, as well as the country with the highest winnings.

# Find the total data with the max and average per country
total_money <-sum(cs_earn$winnings)
total_money<-sprintf("$%.0f",total_money)
paste("The total winnings across all majors is",total_money)
## [1] "The total winnings across all majors is $152108326"
# Find the max value
max_money<-max(cs_earn$winnings)
max_money<-sprintf("$%.0f",max_money)

# Find the name of the country with max value
max_money_country<-cs_earn$country[which.max(cs_earn$winnings)]
paste("The country with the highest winnings is",max_money_country, "with a total of",max_money)
## [1] "The country with the highest winnings is Denmark with a total of $21989055"

Data

The data used for this was sourced from https://www.esportsearnings.com/games/245-counter-strike-global-offensive/countries. This website did not have the data ready for download in a CSV or XLSX format, so I created a CSV file called “cs_earnings” and could copy and past it from the web. The issues that arose were that when i went to merge the data with the map data, there were countries with differing names (United States of America instead of United States). I used the below code to identify which countries names were not found in both columns and manually adjusted in the CSV file the names of the countries.

name_intersection<-setdiff(cs_earn$country,world_map$sovereignt )
print(name_intersection)

Some could not be done without adding the values to another value as they were considered as regions of a larger country (Hong Kong and China).

Distribution of Winnings

Here we will look at how these winnings are distributed across the globe. For this data set we used the rnaturalearth package and merged it with the data set cs_earn to allocate the winnings to each country before plotting them on the graph.

ggplot() +
  geom_sf(data = cs_earn_map, aes(fill = winnings), color = "black") +
  scale_fill_gradient(low = "gray99", high = "red",na.value="white",labels = scales::dollar) +
  labs(title = "Counter-Strike: Global Offensive Winnings by Country", fill = "Winnings") +
  theme(legend.title = element_text(hjust = 0.5,size = 15),plot.title = element_text(hjust = 0.5,size = 17) )

From the above visualisation we can see that the countries with the highest earnings are from regions with larger populations such as Russia, USA and China. There are also countries with a very light gradient or no gradient at all. These countries are in areas where access to high end technology would not be readily available such as Central Africa, South America and Western Asia.

Highest Earning Countries

Lastly we will take a look at the top 10 highest earning countries with winnings from CS:GO Majors.

top10<-head(cs_earn,10)
top10<-top10 %>%
  arrange(desc(winnings))
  
ggplot(top10 , aes(x = reorder(country, -winnings), y= winnings)) +
  geom_bar(stat = "identity", fill = "steelblue") + 
  labs(x = "Country", y = "Winnings (USD)", title = "Top 10 Highest Earning Countries") +
  theme(axis.text.x = element_text(angle = 45,hjust = 1,size = 9), plot.title = element_text(hjust = 0.5,size = 25))+
  scale_y_continuous(labels =  scales::dollar)

Here we can see that Denmark well exceeds every other country, surpassing the next highest earning country Russia, by more than 6 million US Dollars. We also see that seven out of the top ten countries are European nations. This could be due to an established infrastructure within the CS:GO community in Europe, translating into the development of better players. Whilst it seems that the Europeans seem to be the most skilled players in the world, USA and Brazil, being third and fifth respectively, shows that there is potential as to why the spread of winnings across the global, could not become more evenly distributed in years to come.