Counterstrike

A kill-by-kill analysis of a counterstrike game.

data(counterstrike)

Details

E-sports are a form of competition using video games. E-sports are becoming increasingly popular, with high-profile tournaments attracting over 400 million viewers, and prize pools exceeding US$20m.

Counter Strike: Global Offensive (CS-GO) is a multiplayer first-person shooter game in which two teams of five compete in an immersive virtual reality combat environment. CS-GO is distinguished by the ability to download detailed gamefiles in which every aspect of an entire match is recorded, it being possible to replay the match at will.

Statistical analysis of such gamefiles is extremely difficult, primarily due to complex gameplay features such as cooperative teamwork, within-team communication, and real-time strategic fluidity.

It is the task of the statistician to make robust inferences from such complex datasets, and here I discuss data from an influential match between “FaZe Clan” and “Cloud9”, two of the most successful E-sports syndicates of all time, when they competed at Boston 2018.

Dataset counterstrike is a loglikelihood function for the strengths of ten counterstrike players; counterstrike_maxp is a precomputed evaluate, and zacslist the observations used to calculate the loglikelihood function.

The probability model is similar to that of NBA: when a player kills (scores), this is taken to be a success of the whole team rather than the shooter.

File inst/counterstrike.R and inst/counterstrike_random.R include some further randomisation tests and discussion.

The objects documented here can be generated by running script inst/counterstrike.Rmd, which includes some further discussion and technical documentation and creates file counterstrike.rda which resides in the data/ directory.

Counterstrike dataset kindly supplied by Zachary Hankin.

Examples

dotchart(counterstrike_maxp)