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Introduction
ACS, also known as Average Combat Score, is one of the most-used terms to describe the player performance in all facets of the VALORANT scene. There are, however, some flaws in this system, which has been talked about in the professional scene for quite some time already (see Figure 1). With, for example, some agents per definition getting higher ACS scores due to greater fragging opportunities, bringing with it the impression that passive-played agents have less impact due to their lower ACS scores.
In this short research these flaws will be ironed out as much as possible by creating a long overdue measuring system that is already used in many other disciplines; we’ll call it the Relative Performance Score (RPS) for now.
Figure 1. XSET Ayrin speaking about the negatives of the current ACS system.
Method: Average ACS
The general idea of RPS is to find the relative ACS of a player on a certain agent compared to the average ACS on that agent, this will give a value ranging from 0 to around 2.0, with a score of 1.0 being the absolute average. In this manner players can be compared to their real peers rather than players playing completely different roles.
First things first, the Average ACS of each agent needs to be calculated. This will be done using the ACS of all players that have played at least 100 rounds (except for Yoru, Phoenix, Omen, Brimstone and Chamber) against opponents with a ranking of 1600 or higher on vlr.gg in the last 90 days. I decided to only use the last 90 days to keep the rating as in touch with the meta as possible. The Average ACS of all agents can be seen below.
| Agent | Average ACS (last 90 days) |
|---|---|
| Jett | 229.19 |
| Raze | 229.12 |
| Reyna | 239.81 |
| Phoenix | 205.10 |
| Yoru | 209.30 |
| Sova | 192.64 |
| Skye | 186.38 |
| Kay/O | 194.19 |
| Breach | 173.26 |
| Astra | 185.39 |
| Viper | 190.60 |
| Omen | 173.22 |
| Brimstone | 181.37 |
| Cypher | 185.90 |
| Killjoy | 197.66 |
| Sage | 181.63 |
| Chamber | 203.28 |
So, say a player by the name of ‘JoyousWhimsy’ plays Brimstone and gets an ACS of 210, that ACS score will then be divided by the ACS of an average Brimstone player, which is 181.37. This gives an RPS of 1.16 for ‘JoyousWhimsy’ in that particular match, slightly above average. More of these examples and visualisations can be seen below.
Example 1
First, a full match will be reviewed using the RPS to judge player performances more accurately. The match in question is the Grand Final of Champions: Acend – Gambit Esports.
As you can see, the RPS is displayed in the very first column to the left of the agents that were picked. This is to take away the focus from the agents themselves, as the RPS also negates most factors regarding the agent, it’s more about the player and their performance.
This map featured a great performance by Gambit nAts on Viper, which logically grants him a relatively high RPS with 1.77. Another interesting incidence is the fact that both zeek and BONECOLD have a higher RPS than Acend’s cNed, although cNed is higher up on the ACS leaderboard. This is a perfect example of why RPS could (and maybe should) be the standard to which the players’ performance should be gauged.
I won’t add too much text to every single map of this game, since most numbers speak for themselves, but here again: Acend’s zeek has a lower ACS than cNed, although his relative performance to his peers (other Kay/O players) is way better than cNed’s. In this scenario, zeek should clearly be rated higher than cNed.
This is where we get to the well-known otherworldly performance by nAts. With an RPS of 2.12, this is immediately visible. Comparatively, a Jett-player must get an ACS of around 460 to match his performance. On the other side of things, Kiles had an abysmal game with an RPS of 0.23, even though he is also playing Cypher.
This is another map that shows the use of the RPS, with starxo’s performance almost going unnoticed in the ACS scheme of things, even though his performance on Icebox was amazing.
The overall RPS of each player can be seen in the above figure, with starxo being the only player on Acend to deliver a better performance than the average on his agents played. Also, BONECOLD delivered a performance of similar quality to cNed and zeek (see their RPS), but he might get undervalued due to the lower ACS.
On the other side, nAts and Chronicle tried all they could, however it must be said that their RPS might be skewed due to the Fracture game in which they both carried their team to an easy map victory. It might look like all the others underperformed, but a score of around 1 pretty much means a player is doing his job, compared to their peers they’re neither outperforming nor underperforming. The only player that really underperformed in this match was Acend’s Kiles with his highest RPS being 0.71 on Ascent.
Example 2
This is another example on how RPS could be useful, as this diagram shows the nAts’ last 10 maps. This could be used to find tournament MVP’s more easily as well as looking at the current form of certain players. The average RPS of nAts’ last 10 maps, for instance, is 1.15. This shows he’s outperforming the average cypher and viper players.
Example 3
RPS also allows us to compare the significance of star performances, as star performances on Jett can now easily be compared to those of a Brimstone, for example. NV’s yay got an ACS of 449 when playing in the semi-final of Masters: Berlin versus 100Thieves on Haven, one of his all-time best performances. When comparing this to nAts’ Fracture performance in the Grand Final of Champions, it appears nAts’ ACS is significantly lower with 394. However, when comparing the RPS of both performances nAts comes out on top with an RPS of 2.12, where yay had an RPS of 1.96. This just goes to show how outstanding nAts’ performance was and the RPS makes that clear.
Conclusion & Discussion
RPS, or something similar to it, might be the way forward when trying to compare performances of players playing different roles, as the current system gives the wrong idea of “Duelist = Good”. There might, however, still be some flaws in the proposed RPS system. As this system doesn’t take all variables into account just yet. One improvement that can make the system quite airtight is to create a separate Average ACS for each agent on each separate map, to get fairer results on maps where certain agents excel (e.g. Raze on Split).
I hope you liked the write-up. Any feedback is appreciated!
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