In my new article I continue to study flashbangs usage with programming and data analysis. This time I download all the demos from Eleague Major 2018 New Legends stage in order to find out the most efficient flashbangs used by participants and show how do they throw it.
Same method is also used in our upcoming open service for CS:GO demos analysis. Feel free to register here
My approach for the article is pretty simple:
- In order to define “best” grenades/teams calculate some efficiency metrics
- See who is a leader in these terms
- Show some examples of their “repetitive” grenades leading to a notable success.
Idea is that each team has flashbangs that they throw round by round, game by game. This means there are patterns in terms of where player stands and places he throws a grenade. Given that it becomes possible to find out such repetitive grenades automatically using mathematical approach called Clustering
I use two very basic and natural metrics for the flashbangs: flash duration time and flash-to-kill conversion. However, there are different ways to calculate them
One could simply calculate overall sum of them and compare those numbers but it makes sense to average overall sum by number of rounds played and number of flashbangs used
Many people notice how well prepared Space Soldiers are in terms of aim but charts above show they are good in flashing too. Interesting note is that Astralis were not efficient with their flashbangs during a group stage having 14-16th places in all fb ratings.
Now let’s take a bunch of Space Soldiers demos and see how flashbang Clustering works. I took 2 of their mirage demos since its the only map they played twice during the Group Stage. I would be looking for clusters that have non-zero flash to kill conversion
Here are two flashbangs that SS seem to use on a regular basis
Same approach works for any given team or player. Here are two clusters for SK on mirage. Note how you become able to compare different ways to throw a flash over T ramp
I could continue but I guess you’ve got the idea. Upload a bunch of demos, process in a correct way and boom you’ve got grenade clusters.
Here’s how our csgo analyser is going to use that data: based on a single demo (say you upload your game) for all of your grenades analyser could see if there is a cluster that your grenade belongs to most likely and measure your efficiency metrics. If you didn’t do well you get an advice on how you could throw that particular grenade better.
Credits to @yohgcsgo from SixteenZero who published similar stuff before I did