Tracking Down the Rainbow Six Siege Exploiters: A Data Analysis Case Study

In mid-May of 2024, Rainbow Six Siege’s Ranked playlist suddenly became unplayable. Matches were ending in seconds, lobbies were crashing mid-round, and some players were gaining free wins without ever shooting an enemy. Content creators were telling people to stop queuing altogether. My inbox filled with messages asking what was going on.

What looked like chaos to players was actually a very consistent pattern of behavior buried in the game’s data — and I wanted to find out who was responsible, how widespread it was, and whether the exploit left a measurable footprint.

This article is the story of how I analyzed 1.47 million Siege matches, identified the signature of a game-breaking exploit, and uncovered the small but coordinated groups who abused it repeatedly over a four-day period.

Why Did I Get Involved?

The Exploit: What Actually Happened

The exploit first surfaced on May 12th, 2024. A group discovered that a specific sequence of actions could crash the opposing team’s clients and force a surrender victory.

The sequence worked like this:

  1. A player damages a teammate’s shield or commits a teamkill.
  2. Four teammates instantly leave the match.
  3. Those four players rejoin.
  4. Once they are back in the lobby, the original teamkiller/shield-damager leaves last.
  5. That final leave crashes the enemy team’s session.
  6. The match ends as a surrender victory for the exploiters (end reason ID = 22).

The shield-damage variant appeared first, likely because it was easier to reproduce. As more people learned the method, teams began banning shield operators — but this only slowed things down until exploiters shifted to a teamkill-triggered version.

Within hours, Ranked integrity collapsed. Within days, the exploit was everywhere.

Collecting the Dataset

To measure how widespread this behavior was, I collected the post-match data for every Ranked game played between May 12th and May 16th, which totaled 1,478,229 matches.

Siege’s post-match telemetry contains, for each round:

  • Player kills
  • Deaths
  • Assists
  • Teamkills
  • Operator selection
  • Leave/rejoin encoded as operator ID values
  • Round timing and score data

And at the match level:

  • Start time / end time
  • Final score
  • Match end reason

This provided enough detail to detect the exploit patterns if they existed.

Translating the Exploit Into Data

After reviewing many matches manually, the exploit produced a clear signature across three signals:

1. Mass leave + rejoin events

All five members of a team leaving within the same round is almost nonexistent in normal play.
Clusters of 3–4 could occur accidentally, but 5-stack leave/rejoin events are definitive.

To avoid false positives, the final dataset used only 5-player definitive cases.

2. A trigger event: shield damage or teamkill

Exploit rounds almost always contained:

  • Shield operators taking friendly damage
  • Or a teamkill event (RFF)

These events rarely coincide with mass disconnects organically.

3. A premature surrender end (reason ID = 22)

Matches commonly ended:

  • In Round 1 or 2
  • Immediately after the suspicious round
  • With surrender reason 22

When all three signals aligned, the match was almost certainly an exploit.

Building the Detection Algorithm

I wrote a script to process all 1.47M matches and flag cases where:

  • Five players on the same team left within a single round
  • A teamkill or shield-damage event occurred in the same timeframe
  • The match ended early with reason 22
  • The suspicious round immediately preceded the match end

The output was a list of playerID : flaggedMatchCount pairs.

Ambiguous 3-stack cases were deliberately excluded from the final list; they often represented innocent players placed in compromised lobbies multiple times.

The final list was extremely low in false positives and highlighted only clearly coordinated exploiters.

Verifying the Algorithm

To confirm accuracy, I manually inspected a sample of flagged matches:

  • Checked for leave/rejoin operator IDs
  • Verified trigger events
  • Reviewed round timing
  • Confirmed surrender endings
  • Cross-referenced live-match logs I captured during the incident
  • Checked if high-frequency offenders queued together in other matches

The exploit signature was consistent everywhere it appeared.

What the Data Shows

Most players flagged once were simply victims of matchmaking — placed into games with exploiters.

But a small set of accounts appeared:

  • In dozens of definitive exploit matches
  • Across multiple days
  • Often with the same group of players

Here is a sample of high-frequency offenders:

33██████-████-████-████-████████09c3 : 57
70██████-████-████-████-████████27f8 : 45
c3██████-████-████-████-████████54ae : 45
02██████-████-████-████-████████80f0 : 44
2e██████-████-████-████-████████3942 : 43
f2██████-████-████-████-████████526a : 41
76██████-████-████-████-████████75e8 : 40
d4██████-████-████-████-████████4b97 : 40

Why Some Players Rushed to Exploit It

It was visible during the gathering of data, that Ubisoft would take some time to patch the exploit, and yet some players were rushing to use it as much as possible in the meantime. It may seem counterintuitive to rush to use an exploit that is far from being patched, but there is a reason for this. In Rainbow Six Siege, Ranked Seasons last for a limited time, and at the end of each season, players are rewarded with cosmetic items based on their performance during that season. These rewards are highly sought after by players, as they are often exclusive and can only be obtained during that specific season. It acts as a form of status symbol within the game community.

The statistic that Rainbow Six Siege uses to determine these rewards is called MMR, which is a measure of a player’s skill level based on their performance in ranked matches. The higher a player’s MMR, the better their rank, and the more prestigious the rewards they can earn at the end of the season. But the issue lies with the fact that Ubisoft check a player’s peak MMR to determine their rewards, thus allowing players to cheat and exploit, knowing that if they are caught without being banned rolling back their MMR will not affect their rewards. Obviously this is not an intended behavior, but it is a loophole that some players took advantage of.

Did Ubisoft Roll Back the ELO?

Initially, nothing visible happened.
But months later, many high-frequency exploiters showed:

  • Peak ranks far above their final ranks for the season
  • Sudden drops in visible MMR
  • Evidence consistent with deferred rollback

Whether hidden MMR was also adjusted is unclear, but visible rollback did occur.

Takeaways: What This Case Study Shows

This wasn’t about naming specific players.
It was about demonstrating how telemetry can expose large-scale behavior patterns.

Three key lessons emerged:

1. Exploits always leave data fingerprints.

Even chaotic exploits produce consistent mechanical traces.

2. Large datasets make suspicious patterns impossible to hide.

In 1.47M matches, coordinated abuse stood out dramatically.

3. Data analysis is a powerful integrity tool.

With the right approach, developers can detect:

  • Emerging exploits
  • Repeat offenders
  • Ecosystem impact
  • Exploit diffusion patterns

Final Thoughts

Rainbow Six Siege is a game built on precision, timing, and mechanics — and this exploit followed its own mechanical pattern.
By breaking that pattern down into measurable signals, it became trackable, quantifiable, and ultimately traceable to specific groups.

This investigation sits at the intersection of reverse engineering, data science, and player-behavior analysis.
And it shows just how much you can uncover when you treat game telemetry as a truth source.