Riot clarifies about their game-balancing policy in VALORANT

Rifat Bin Yusuf
By Rifat Bin Yusuf
3 Min Read
Image Via: Riot Games

Riot Games has published a report that summarizes how utilizing in-game data enhances the overall stability of VALORANT.

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VALORANT has reshaped the landscape of competitive FPS within a short time. Launched in June of this year, VALORANT was all about infiltrating the very concrete domain of Counter-Strike. Instead of being just a competitor, the game rose to a new height after successfully concluding its first major tournament, the First Strike.

It’s been six months since the global release of VALORANT. The early version of the game has endured instability issues, specifically with the half-baked Agent pool. According to the community, particular Agents had non-competitive features embedded into them. During the beta stage, every professional team picked up Sage to fortify their lineups.

Riot’s in-game data collection procedure

A brief report composed by researcher Coleman “Altombre” Palm and analyst Brian Chang from VALORANT’s Insights team explains how developers utilize in-game data to improve specific elements of the game. Initially, developers collect the data to perform fundamental analysis. A preliminary decision is taken based on a particular agent’s win-rate, given that they are not present on both teams. If both teams pick up similar agents, then the data won’t make it out of the filter for further analysis. 

valorant game balancing policy
Source: Riot Games

Suppose both teams pick up Phoenix; in that case, the data will be invalid because the win-rate won’t prove which agent is packing overpowered abilities. Instead, developers will extract data from match-ups that don’t include common agents on both teams. For example, if the winning team has a Skye, the data will increase Skye’s win-rate by a certain percentage. Here, Skye is a differentiating factor & her abilities will get a recheck if she continues to ride on a winning streak. However, the Insights team also added that data is not the only determinant; it’s just one of many. 

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On top of all the statistics & data compilation, the Insights team also highlighted the impact of linear feedback from players. The dramatic rise and fall of Sage is one example of all the procedures mentioned above. However, VALORANT is still far from perfect, and some Agents are still causing significant disruption in the game’s competitive portion.

A competitive Sova main Valorant player who wants to write stuff and keep people engaged with gaming. I grew up with a parallel passion for writing and gaming.