In addition to shooting with different kinds of firearms, the attackers and defenders have access to a few items that help them achieve their goals. Two notable examples of items are:

• Smoke screens create temporary walls on the playing field. The smoke obscures the view, but it is also difficult to pass through in practise because a player outside the smoke has better visibility into it than a player inside of it has out. This puts the player inside the smoke at a gunplay disadvantage.
• Flashbangs temporarily blind and deafen players that look at it as it detonates. While smoke screens have roughly the same effect on all players, skillful deployment of flashbangs can have the blinding effect on the opponent without any effect on the team that used it.

The reason I bring this up is that using these items can be counter-intuitive2² One of my favourite aha! moments was when I realised that if my opponent has used a smokescreen to deny me entry somewhere, I can often deploy a smokescreen of my own just beyond that of my opponent, which gives me a protective wall between myself and my opponent as I wade through their smoke, and then gives me the choice of which angle I want to engage them from around my smoke. It’s one of those Columbus’ eggs that sound obvious once you’ve heard it – yet most people don’t think of it before they hear it. and a skill unto itself, completely separate from the shooting. These items (as well as the firearms used) need to be exchanged for in-game money, which is earned primarily by scoring points. Economising on in-game money is yet another skill that matters a lot – the right firearms and items give such an advantage it is often worth not buying anything one round (and run a large risk of giving the opponent a point) in order to be able to buy fully the next round.3³ And, perhaps obviously, the team needs to coordinate buys.

When a player dies, their firearm and items appear on the playing field for other players (including oppoents) to pick up. If a player survives, they carry their firearm and items with them into the next round.

In summary, one could say there are five resources both teams need to manage and trade off against each other during a full match:

• Player health: many weapons in Counter-Strike kill with one well-aimed hit, but health still matters when using weaker weapons, or when fighting from behind cover.
• Security areas: the locations one team denies the opponent access to.
• Information: knowledge of opponent movements.
• Time: both defender and attacker objectives are timed4⁴ There are some nuances here I haven’t explained but I don’t think they matter for the thrust of the article..
• Economy: consisting of firearms and items, as described above.

Some people would lump together security areas and information into the single umbrella of map control but I think it’s more clear to separate them.5⁵ It makes a difference for in-game decisions. Spotting for information can be done in a way that doesn’t really let the spotter shoot anything, but also significantly reduces their risk of being shot. Spotting to be able to shoot, i.e. deny access, requires exposing oneself to damage much more.

As examples of basic and common tradeoffs,

• A team yields one of their security areas to conserve health for future engagements.
• A team sacrifices player health to gain information.
• A team spends time to extend a security area.
• A team sacrifices information gathering to have more time to execute on their objective.

There are many variations on this.

I have downloaded (well, scraped with some JavaScript and regexes) data from 28 matches I’ve played, so there are a total of 280 records, with each record specifying the per-round statistics of the above variables for each player in each match.

We can already conclude that matches where a player has killed the entire opposing team are rare enough that we shouldn’t include them in our analysis.

paste("Records with five-kill:", sum(df$mk5 > 0))

Records with five-kill: 0

How about four-kills, where one player kills everyone but one of the opposing team?

paste("Fraction of records with four-kill:", sum(df$mk4 > 0)/nrow(df))

Fraction of records with four-kill: 0

Surprisingly common! Let’s keep those for now. The other levels of multi-kill are also common.

Now, to get a better feel for the data, we can make a scatterplot of kill rate against Leetify rating.

Oh, wow. Already we get a clue as to how hltv concluded that the k/d ratio makes up 75 % of the explanation for winning! Just kill rate alone seems very strongly correlated with contributing positively to the team.

We do have some outliers:

• There is one player in the data set that has 0 kills and 0 Leetify rating. If we look up the game in which that happened, that was a player that disconnected early and truly did not contribute at all. We can remove that data point for a more solid analysis.

• Then there’s the group of five players that have a Leetify rating greater than 15 and a kill rate greater than 1.35. I looked into what set these apart, and it seems to be a combination of a few factors. Mostly, they got their kills near the end of the round when information is sparser and the element of surprise more important. Coupled with luck, good mechanics, and, in one case, cheating, that is apparently a really good strategy at my level of play.

  But! In this case we’re interested in how we can improve our early-and-mid round play, so we’re going to strike these records from the dataset also, to avoid their leverage tainting the analysis we want to do.

df <- df[df$kpr > 0 & df$kpr < 1.35,]

That’s better. Now let’s validate the other variables.

• Kills strongly correlate with rating. (+0.85)
• Deaths correlate negatively with rating. (-0.50)
• Damage dealt correlate strongly with rating. (+0.81)
• Multi-kills correlate with rating, but decreasingly so:
  • 2K (+0.61)
  • 3K (+0.51)
  • 4K (+0.29)

Perhaps surprisingly, assists are not correlated with rating at all! This makes sense though, when one sees an assist as a failed kill. It does, however, hint that getting the kill may be vastly more valuable than just dealing damage.

It is also worth mentioning that while damage dealt is strongly correlated with rating, it is also strongly correlated with kills (+0.91) so they are, practically speaking, the same variable. Coupled with the observation above – that kills are much more important than assists – we are getting quite a case against damage being significant.

Let’s try on a first model, then. How well does kill rate predict rating?

summary(lm(rating ~ kpr, df))

Coefficients:
            Estimate Std. Error t value Pr(>|t|)
(Intercept) -13.3442     0.5030  -26.53   <2e-16 ***
kpr          18.3404     0.6764   27.11   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.74 on 272 degrees of freedom
Multiple R-squared:  0.7299,	Adjusted R-squared:  0.7289
F-statistic: 735.2 on 1 and 272 DF,  p-value: < 2.2e-16

In more mathsy form, where \(L\) is Leetify rating and \(k\) is kill rate:

\[L = -13 + 18k\]

The R² metric measures how much of the variation of the rating is explained by the model, and it is already up there near 75 %. For all its tactics and resource management, getting kills seem to be the main component of good Counter-Strike play. But that does not mean to become a good player, one needs to rush into more firefights! It might be that the players with high rating perform other actions that in turn put them in a good position to get kills. Important distinction.

But it also means that if a team mechanically outclasses their opponents (i.e. shoots at them better), then a very large amount of strategising is needed to overcome that disadvantage.

When one engages an opponent in a firefight, one runs the risk of dying oneself. We’ll add death rate to the model to see what happens.

summary(lm(rating ~ kpr + dpr, df))

Coefficients:
            Estimate Std. Error t value Pr(>|t|)
(Intercept)  -3.8132     1.1600  -3.287  0.00114 **
kpr          16.6049     0.6274  26.467  < 2e-16 ***
dpr         -11.3417     1.2755  -8.892  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.416 on 271 degrees of freedom
Multiple R-squared:  0.7909,	Adjusted R-squared:  0.7894
F-statistic: 512.6 on 2 and 271 DF,  p-value: < 2.2e-16

In mathsy terms, we now have

\[L = -4 + 17k - 11d\]

Adding death rate did not increase the explainatory power of the model by a lot (R² went up by 7 percentage points)7⁷ This may at first be confusing, but it’s as simple as deaths being somewhat correlated with kills, so to a small extent the death variable was already included in the model through the proxy of kills., but it did something useful to the intercept: it put it closer to zero.

We would expect the intercept to be close to zero, because a player does not significantly disadvantage their team just by existing. The way we can interpret this is that the first model embedded the assumption of a certain number of deaths into the intercept. When we assume that a player that exists will die the average number of times, that makes their existence negative. Including the death rate as a separate variable gives players a more neutral existence, and then they can go out and make their own mistakes.

We already had a strong suspicion from the correlations that assists and damage dealt would be unimportant variables. This continues to be the case; they don’t add any explainatory power to the model, and assists are even Fisher insignificant.

Damage was a curious case, though. If we scale damage up to have similar-sized coefficients as the other two terms8⁸ Specifically, divide it by 100 because it takes 100 damage to kill a player., and call the variable \(a\), the model with it included turned out to be something like:

\[L = -4 + 10k + 7a - 11d\]

In other words, the only effect including damage had was shifting some of the coefficient from kills to damage. If ever two variables sort of measured the same thing, these two are them!

Multi-kills are also strongly enough correlated with kills and they appear not to add anything useful to the model. That’s good to know, though – having two kills in two subsequent rounds is no better or worse than having two kills in one round and none in the next.

And that’s it. Those were the variables in the dataset I based this on. And they do, to be fair, explain 80 % of the Leetify rating, so it’s not a bad start.