Apex Legends™: Anti-Cheat Update

Time for another anti-cheat update, Legends. With Showdown underway, we wanted to share some insights into last season, on-going efforts, and a quick look ahead. The team has been working hard behind the scenes as we continue making progress in our ongoing efforts against unfair play and bad actors. Here’s a TLDR of what we’re covering in today’s dev update:

• A Look Back at Prodigy: Total Bans (TB), Match Infection Rate (MIR), and Reports Per User (RPU) all saw fluctuations compared to the previous two seasons (see chart below). This is partially thanks to an additional data stream which improved tracking and expanded our console detections. We’ve also continued work towards cracking down on third-party macro devices.
• Ongoing Efforts: breaking down TB, MIR, and RPU—three pieces of our regular targets. We compile these utilizing several different layers which can result in varying values (examples below). MIR focuses on Ranked, while TB and RPU are inclusive of all modes.
• Recent Focuses: the launch of a new tool system has increased our adaptability and will continue to evolve as cheats do. It helps us to further action some of our top priorities including teaming and third-party bots. We’ve seen a decrease of 33% with teaming reports since February, and will continue to monitor and train the tool as results continue. Aim bots and anti-recoil are also a key focus that we’re actively developing and testing in.

As we mentioned with our last dev updates, we’ll continue to share details whenever possible as they become available.

A LOOK BACK AT PRODIGY

Overall, last season saw an increase in total bans. Match Infection Rate and Reports Per User also saw some slight fluctuations comparative to last season that varied depending on platform. We’ve also recently unlocked a new data stream allowing us to consider more console bans, making the numbers below more encompassing of our action comparative to our previous data shares. Improved tracking on these have let us make more strategic decisions around effort prioritization and resourcing, including allocating additional team members to higher-infected regions. 

Previously, our data has been primarily on PC due to focusing cheat programs that don’t exist to the same degree on consoles. We’ve begun including console accounts in these stats as well as we’ve ramped up detections and actions against teaming, and work toward cracking down on third-party macro devices. For the chart below, Match Infection Rate and Reports Per User reflect the first two weeks after each launch, seasonal or mid-season patch. Total Bans reflects the duration of the entire half of the season.

ON-GOING EFFORTS

We’ve mentioned in previous updates that anti-cheat is a never-ending process that has a plethora of pieces that are constantly in motion. Total Bans, Match Infection Rate, and Reports Per User are just a few of the puzzle pieces, but we wanted to share a bit more detail on how those take shape. 

• Total Bans: total number of suspensions (temporary + permanent) on player accounts via either automated systems or manual reviews. We use these in conjunction with other avenues, such as RP wipes, to take action against bad actors.
• Match Infection Rate (MIR): percentage of matches in which at least one player was reported 3 or more times and banned for cheating within the last 14 days. Apex Legends has a high player count per match (comparative to some competitors) which can contribute to higher MIR. It only requires one of the 60 players in a BR match to be cheating for a match to be considered “infected,” and even then you may not encounter the cheater directly or experience their impact on the match.
  • A key way we’re able to drive down MIR is by improving our time-to-ban. Detecting cheaters quicker means they’ll impact less matches.
• Reports Per User: sum of all reports against players reported three or more times in the preceding 28 days divided by the number of active players in that same time frame. 
  • 3+ reports are not required before a ban is placed. This is the general guideline used to determine when someone is negatively impacting the player experience, regardless of actions taken due to reports. Including a 3+ report threshold is important as it often catches those who may have been playing fairly initially before switching to cheating.

All metrics, including the ones we mentioned, are compiled using several layers including data centers, game modes, match tiers, report types, and more. This means that values can vary significantly per player, per match, per session, and even per region depending on the cross section of data. Here’s some variable examples:

• Match Infection Rate is typically the highest in Ranked where most cheaters congregate. 
  • Unranked Modes have a significantly lower MIR (between -50% to -80%) compared to Ranked.
• MIR also increases as you move up Ranked Tiers. Diamond and Master-level matches are twice as likely to be infected than the global Ranked Infection Rate, whereas lower-tier matches are significantly below the average.

RECENT FOCUSES

We’ve compiled some brief updates on a couple of our recent focuses because we want to give as much transparency as we can to our community.

AN ADDITIONAL SYSTEM TOOL

We launched a new system in season 23 that assists our actions against bad performers by analyzing various types of unfair play to each player based on gameplay indicators. It’s adaptable and will evolve as new variants of cheating are introduced. 

Looking forward, we’re hoping to expand these efforts to include input manipulation and anti-recoil. 

TEAMING, THIRD-PARTY BOT DETECTION, & MORE

On the detection side, we’ve been making progress in a number of areas and added a report option for “Wallhacking/Impossible Gamesense” thanks to your feedback in our Anti-cheat and Matchmaking AMA back in May. Here’s an update on some of our top priorities: 

• Teaming: since we last spoke on teaming, we’ve continued to improve this model over time and have observed further dropping of report rates (-33%) when compared to February. With the launch of our additional tool, we’ve been able to increase our detections based on more gameplay indicators. 
• Third-party Bots: after refining the gameplay indicators, we were able to use our tools to target and remove “bot farms” and further train our additional tool for future detections—including an in-progress automated Bot Detection model. We’ll be closely monitoring results and you may see the impact on MIR and player reports in our future data shares.
• Aim Bots & Anti-Recoil: detection in this area is a critical area of focus for us and we’re developing in-house gameplay detection models for aimbots and anti-recoil devices. 

SEE YOU ON THE DROPSHIP

We’ve said it before and we’ll say it again: Apex Legends should be fun and competitive without any loss to the integrity of the game. As we continue to make progress, you can expect continued dev insights on anti-cheat and other game impacting updates across blogs, reddit, Steam, and X/Twitter throughout the year.

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