Anti-Boost: 296,416 Accounts and Riot Games' Silent War to Keep the VALORANT and League of Legends Ladder Clean

My first big bet did not come from bravery. It came from the crowd's mistake.

But this time, the story is not in a football match. It is in a table of numbers that most ranked players ignore when they open their client at midnight: 296,416.

That figure is the number of accounts Riot Games claims to have actioned for rank manipulation in VALORANT and League of Legends. Not matches, not temporary bans. Accounts — a different unit of measurement from how the community usually counts. When an ordinary player hears "Riot is cracking down on boosting," they picture a few caught streamers. When I look at the number, I see a system operating at industrial scale, confronting an underground economy also operating at industrial scale.

I have tracked matches and ranking data tables for years, but most of that time was spent on football and metrics like xG and PPDA. When I shifted attention to how publishers run anti-cheat systems, I realised one thing: the principles are the same. In football, I learned to trust the structure of data over crowd emotion. In esports, I learned to read an official publisher statement like a contract — noting which clauses are written clearly, and which are left blank.

The blank clauses are where the real story lives.

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Context: When rank becomes a form of asset

To understand why Anti-Boost exists, you need to understand boosting as an economic phenomenon, not just a rules phenomenon.

In VALORANT and League of Legends, rank is not just a number on a profile. It is a social signal. In many communities, high rank means invitations to play groups, consideration as an amateur candidate, offers of streaming opportunities, and in some cases, treatment as a convertible asset.

Boosting — the service in which a high-skill player logs into another person's account and plays ranked matches to climb on behalf of the owner — is not new. It existed from the era of the first online arena games. What has changed is its scale and degree of professionalisation.

In markets with large gaming populations, boosting operates as a complete underground industry: price lists by rank tier, guaranteed completion times, warranties, booster rating systems, and even seasonal promotional packages. Buyers pay for a rank they could not achieve themselves. Sellers earn by playing below their true level, usually briefly, with an artificially high win rate.

For publishers, this is a structural problem. A ladder only has value if it reflects true skill correlation. When a significant share of high-tier accounts are climbed by someone else, matchmaking begins to distribute incorrectly. Average-skill players are pushed into matches of mismatched difficulty. High-skill players meet teammates who do not respond to expectations. The experience at every tier is eroded, including for those who never buy the service.

That is why Riot built Anti-Boost.

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What Anti-Boost actually is

Anti-Boost is Riot Games' automated enforcement system, designed to detect and penalise rank manipulation. The point to stress: it is not an anti-cheat programme in the classic sense. It does not target players using interference software. It targets a different class of behaviour — human behaviour, executed through the legitimate game interface.

This matters because it shapes how the entire system operates. Anti-cheat for software is a binary problem: the programme is present or it is not. Anti-boost is a probability problem: reading intent from behaviour.

Riot defines a clear set of violating behaviours. First, direct boosting: a high-skill player logs into another's account, plays ranked, and earns rank points for the owner. Second, account buying, selling or transferring — a commercial transaction on an account, which the publisher treats as the most serious form of service violation. Third, intentional deranking — deliberately losing to lower one's rank, usually to play at a lower tier for easier wins, or to prepare for a reverse boosting session. Fourth, using a secondary account with a booster's assistance to climb, a hybrid of smurfing and boosting.

There is a boundary Riot draws very clearly, and this is the most notable detail in the entire approach. Self-created and self-operated alt accounts are normal activity. Riot does not action players merely for having multiple accounts. It acts when it detects intent to manipulate rank.

In other words, the system targets intent, not the existence of alt accounts. This is a narrow design standard, and it has both advantages and consequences.

I do not watch football to enjoy it. I watch it to test a long-term hypothesis.

Here too. I do not read Riot's statements to know what they say. I read them to know what they do not say.

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The penalty ladder: Four tiers, one escalating logic

Riot describes a penalty ladder structured to escalate with severity and recidivism. A close read reveals their design thinking.

Tier one is basic handling of detected manipulation. Consequences include cancelling rank points and rewards obtained from cheating, returning the account to its pre-manipulation rank, and temporary suspension. This is a rollback mechanism, not merely punishment. Notably, Riot cancels rewards, not just rank points. This aims to remove the economic incentive — if the purpose of boosting is to capture seasonal rewards or limited items, revoking rewards attacks that objective directly.

Tier two is repeat offence. Ban duration increases. This detail reveals something the publisher perhaps need not concede: a non-trivial recidivism rate exists. If every violation were a first offence, escalation rules would be unnecessary by design. Building escalation into the system is an implicit statement that a portion of penalised players will reoffend.

Tier three is account buying/selling or transferring, or intentional deranking. For these two behaviours, penalties can reach permanent ban. This is a key distinction in penalty design: Riot reserves the heaviest measure for behaviours with clear commercial motive. Account transfer is a transaction. Intentional deranking is preparatory behaviour for a transaction or in service of one. Both are commercial in nature, and both receive the highest penalty.

Tier four is joint liability. This is the most contentious part. Riot states that a booster's main account and teammates who frequently pair with them may also be actioned.

Read together, these four tiers show a system designed not to punish individual incidents, but to change the incentive structure of an entire underground market.

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The detection mechanism: Reading behaviour, not identity

The most professionally important point Riot's statement discloses is here: detection operates on behavioural signals and telemetry, not on direct proof of account ownership.

What does this mean in practice?

In most cases, the publisher cannot conclusively prove that the person at the keyboard in account X's twelfth match is not the owner. There is no camera, no mandatory biometric check, no real-time identity verification at global scale. So the system must infer from behavioural traces.

Those traces include sudden shifts in play patterns: an account with stable win rate and personal performance metrics at one level abruptly moving to a much higher level within a short window. They include differences in role selection, movement, and decision-making across situations. They include behaviour patterns tied to login frequency, play hours, and correlations with other accounts.

This is a probabilistic pattern-recognition problem, and like every probabilistic problem, it has two error types: misses and false positives.

Riot concedes it is improving match-level boosting detection based on characteristic signs. The phrasing is careful. It implies the current version of the system is not yet complete at the match-level detection layer, and that is why the publisher keeps investing.

Amid the cheers of Russia, I heard a number whisper — and it was more accurate than the crowd.

In this case, the whispered number is not 296,416. It is that the figure was not published with any baseline data.

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The problem with the cumulative figure: No baseline, no trend

This is the analytical point I want to give the most space to, because it relates directly to how I read data daily.

The 296,416 figure is cumulative. It states the total accounts actioned over a period, but that period is described imprecisely.

A cumulative figure without a baseline cannot establish a trend. This is a basic principle of data analysis. If I know a team scored 60 goals in a season, I cannot conclude their attack improved or declined unless I know how many they scored the previous season, across how many matches, with what squad structure.

Here, a number of actioned accounts comes with no comparison to an earlier period. There is no country or regional breakdown. There is no separation between VALORANT and League of Legends.

I regard pooling two titles into a single number as an analytically weak reporting choice. VALORANT is a tactical shooter; League of Legends is a multiplayer online battle arena. The two have different ladder structures, different community cultures, different boosting-market economics, and different rank-inflation pressures. Pooling them obscures the very differences that matter most.

If the purpose of publishing is to show the community the scale of the problem, pooling may make sense for communications. But if the purpose is to demonstrate that the system is strengthening, pooling backfires analytically, because it offers no basis for comparison.

This is where I want readers to pause. When a publisher releases a big number, the natural reflex is to believe the measure is working. But a big number has two entirely opposite interpretations: the system is detecting better, or the problem is bigger. Without a baseline, the two cannot be distinguished.

In football, the only thing worth trusting is what the crowd has not yet seen.

In an enforcement report, the only thing worth trusting is what the report does not say.

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Joint liability: The system's biggest blind spot

This is the part I consider the critical counter-argument of the whole analysis.

Riot states that teammates who frequently pair with a booster may also be actioned. The phrasing seems reasonable on the surface. If a player repeatedly pairs with a boosting account, they may know and participate.

But there is a large gap between possibility and conclusion.

Imagine an ordinary player who finds a good teammate through random matchmaking. They play together for a few evenings, click well, climb together. That teammate turns out to be operating a boosting account for someone else, or using a secondary account to carry a third party's rank. The ordinary player has no way to know.

In this scenario, a "frequently paired" standard could penalise someone who violated nothing.

The statement describes no appeal mechanism. No pairing-count threshold is stated. No criterion distinguishes a knowing accomplice from a swept-up player.

This is the type of risk I call, in risk analysis, false-positive risk. It is not theoretical. It is a direct consequence of a broad joint-liability design with no release valve.

Why did Riot choose this design? There is a defensive logic. If only the directly manipulated account is punished, a booster can run multiple accounts simultaneously, diversifying risk. One banned account does not affect the broader operation. By extending scope to the main account and frequent teammates, the system creates a higher risk level per boosting session, raising the expected cost of the activity.

This is a rational strategy in the economics of crime. But its price is the possibility of actioning non-violating players.

When the stadium was empty, I realised I had bet on a legend for four years.

Here, when an enforcement system expands scope without expanding transparency, the community may lose faith in the very system meant to protect them.

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The intent-based standard: Flexible or arbitrary?

Riot chose an intent-based standard. Self-operated alt accounts are not actioned. Rank manipulation is. This is a deliberate design decision, and it protects a large number of legitimate players. Many gamers have multiple accounts for entirely valid reasons: playing with friends at different skill levels, testing new playstyles without affecting their main rank, or simply separating serious play from casual play.

A hard rule banning all alt accounts would destroy the experience of this group. Riot avoids that.

But an intent-based standard has an inherent drawback: it is hard to enforce consistently and hard to explain transparently.

Intent is not directly observable data. It must be inferred from behaviour. And when a system infers intent from behaviour, two players with identical surface behaviour can receive two different conclusions, depending on contextual factors the system knows but the community does not see.

From the player's viewpoint, this creates a sense of inconsistency. Two cases that look alike from outside, but one is penalised and one is not. The community has no way to verify the correctness of the decision if the publisher does not disclose details.

This is the core tension of every intent-based enforcement system: the more flexible, the harder to be transparent; the more transparent, the less flexible.

Riot chose flexibility. It is a defensible choice. But it shifts the burden of proof from publisher to community, and that can become a problem when a high-profile false positive occurs.

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Controlling detection and adjudication: Concentrated power

Another detail worth noting on governance: Riot controls both the detection and the adjudication mechanism. No independent third-party appeals body is described.

This is not unusual in gaming. Publishers typically retain final authority over accounts in their ecosystem, as part of the service agreement players accept on sign-up. But controlling both ends of the process has consequences.

In traditional justice systems, separating investigative and judicial bodies is a foundational principle. The aim is to reduce systemic error risk: one body can err, but two independent bodies erring identically is less likely.

In gaming, no such requirement exists. The publisher is lawmaker, enforcer, adjudicator, and publisher of results. This is a fully concentrated structure.

Operationally, this structure has advantages. It enables fast feedback, flexible adjustment, and consistent global rollout. A system with an independent appeals body would be slower and far costlier.

On community trust, this structure has disadvantages. There is no channel for a player who believes they were wrongly actioned to seek independent review. They depend entirely on the publisher's internal process.

This is a trade-off every major publisher must make. Riot chose operational efficiency. It is a defensible choice, but it places transparency responsibility on the publisher itself.

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Self-reported data: The problem of no independent audit

One methodological point must be stated clearly: the 296,416 figure is self-reported by Riot. It is not audited by an independent third party.

This is not an accusation. It is an observation about data limits. When an organisation publishes figures about the performance of its own system, there is a natural incentive to present favourably. That is the nature of every organisation, not only in gaming.

In sports analysis, I apply the same principle. When a club publishes performance data, I cross-check against independent sources. When a league publishes attendance figures, I seek verification. Self-reported data has value, but its value depends on whether it can be independently verified.

Here, there is no independent verification. No third-party data provider tracks boosting bans. No external body can confirm the figure.

This does not make the figure worthless. It makes it bounded. It tells us Riot wants the community to know a big number. It does not tell us whether that number reflects reality accurately.

In risk analysis, I mark this as a point to track. If in subsequent reporting periods Riot maintains periodic publication and the figures are comparable, we will have a basis to assess trend. Until then, the current data is a single pixel, not a plotted line.

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Transmission: From the ladder to the whole ecosystem

Now I move to the part I consider the most durable analytical value: the transmission of Anti-Boost effects across layers of the esports ecosystem.

The upstream layer is the publisher. Anti-Boost is an investment in trust maintenance. The ladder is the data foundation of the entire esports ecosystem. Professional teams, academies and scouts rely on the ladder as one input for talent evaluation. If the ladder is contaminated significantly, signal quality falls.

The midstream layer is the underground economy of boosting services and the account market. Heavy action against account buying, selling and transferring attacks the supply side of this economy. If buying an account becomes riskier, the expected cost of the transaction rises, and a portion of demand may leave the market. The effect is indirect, but it can be meaningful.

The downstream layer is player experience and related channels. A cleaner ladder improves matchmaking quality for everyone. It also improves the quality of the path from solo play to professional competition, as amateur talent is discovered based on real performance rather than inflated rank.

The peripheral layer is publisher-competitor dynamics and general public trust in the legitimacy of online competition. Riot publishing its enforcement data functions as a reputational signal, to players and investors, that ladder integrity is actively managed.

At this layer, an interesting comparison with other publishers emerges. Some titles are criticised by communities for lacking transparency in cheating enforcement. Riot publishing enforcement figures, however verification-bounded, places them in a relatively better communications position. It may be a small competitive advantage.

PPDA is a lens — through it, I saw Morocco in the semi-finals two months ahead.

In this case, the metric to track is not a technical one, but the enforcement figures published periodically. They are the lens for seeing the trend.

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The dynamics of the boosting economy: Why the problem will not vanish

To properly gauge the severity of the problem, one must understand the economic dynamics behind it.

Boosting exists because there is demand to pay for rank. That demand does not vanish when a publisher tightens enforcement. It changes form.

When detection pressure rises in the direct channel — a high-skill player logging into another's account — activity tends to shift to harder-to-detect channels. Common forms include organised group play, where a booster supports a buyer group in team matches, blurring the line between legitimate support and boosting. They include coordinated deranking to create low-tier accounts with high MMR, a valuable market asset. They include external communication channels, where coordination occurs without leaving traces in the game's chat system.

This is the fundamental asymmetry between enforcement and evasion. The publisher must improve detection for every known and unknown violation form. The violator need only find one form not yet well detected.

Riot concedes this indirectly when it speaks of continuing investment in match-level detection. A publisher that believed its system complete would not need to announce expansion plans.

This is an arms race, and in arms races, the attacking side usually has the advantage in adaptation speed.

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Comparison with football: The same integrity problem

I approach esports from a football background, and there is a notable parallel between the two fields.

In football, competitive integrity is threatened by match-fixing and illegal betting. Governing bodies such as FIFA and UEFA build monitoring systems, partner with data companies, and impose escalating penalties. The fight against match-fixing shares the same asymmetric characteristic: the regulator must monitor every match, the fixer need only find one gap.

In esports, boosting and rank manipulation are the ladder-level equivalent of match-fixing. They distort the skill signal, just as match-fixing distorts match results.

The key difference is scale and speed. In football, each match takes 90 minutes, with a finite number of parties, and regulators have time to investigate. In ranked esports, millions of matches occur daily, with tens of millions of players, and enforcement must operate automatically at that scale.

That is why I find Anti-Boost analytically interesting. It is an example of integrity governance at a scale traditional sports do not face. In football, a referee can review VAR over minutes. In ranked esports, there is no referee per match. The system must be the referee.

VAR does not reduce controversy; it merely shifts controversy from the pitch to the review room and the grey areas of law.

Anti-Boost operates on a similar principle. It does not eliminate cheating; it shifts the battle from manual detection to algorithm optimisation, and shifts controversy from whether a player cheated to whether an algorithm judged correctly.

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The satellite club system and alt accounts: An analogy

There is an interesting parallel between how youth development operates in football and how the account market operates in esports.

In football, the satellite club system lets big clubs circumvent domestic training regulations. A young talent from a small league can be registered at an affiliated club, accumulate playing time, then be promoted to the senior squad when rules allow. That talent becomes a kind of satellite asset, managed through a legitimate legal structure serving a regulation-avoidance purpose.

In esports, alt accounts operate on a logic with similarities. A player creates multiple accounts, each at a different tier, serving different purposes. When managed carefully, these accounts form a set of digital assets operating within a legitimate framework.

The crux: both systems operate in a grey zone between legitimate activity and avoidance behaviour. And in both cases, managing this grey zone is a challenge for the regulator.

Riot chose an intent-based approach, much as football regulators try to distinguish legitimate loans from loans designed to circumvent rules. Both wrestle with the same question: how do you distinguish intent when the external form is identical?

This is why I find governance decisions in esports valuable as reference for traditional sports, and vice versa. Structural problems tend to repeat, differing only in speed and scale.

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Community perception: No counter-argument published

One observation about the nature of this report: it is entirely sourced from the publisher. No community counter-voice is reflected. No false-positive case is mentioned. No appeal controversy is discussed.

This does not necessarily mean those problems do not exist. It means this report does not reference them.

In my analysis, I assess this as a sign of the source's one-sided nature. A balanced report would include player perspective, or at least a statement on the appeals mechanism. The absence of these elements suggests this is a faithful summary of Riot's official messaging, not a balanced investigative piece.

I always apply this principle when reading data from single sources. In my betting analysis, I never rely on one source for an important decision. In sports analysis, I cross-check figures from multiple data providers, because each provider defines metrics differently.

Here, the sole source is Riot. That does not make the information false. It makes the information bounded in scope.

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False-positive risk: An indirectly measurable risk

I want to describe concretely how false-positive risk can manifest, because this is the point readers often find hard to picture.

Suppose a detection system operates on behavioural signals. It flags an account when performance metrics shift according to a pattern learned from historical data. Any machine-learning model has a decision threshold. Above it, the account is flagged. Below it, the account is treated as normal.

With any threshold, there always exist near-threshold cases. These are cases the model is uncertain about. If the threshold is set to reduce misses, the number of false flags rises. If set to reduce false flags, the number of misses rises. This is a fundamental trade-off, not removable.

Now add the complexity of joint liability. When the system flags account A, it may extend investigation to accounts B, C, D that frequently pair with A. If A is a false positive, B, C, D may also be indirectly affected. An error at one point can spread into a cluster of errors.

This is why I regard joint liability as the largest governance risk point. It does not merely raise error probability; it raises the impact scope of each error.

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Impact on the talent-scouting path

A less-discussed aspect is the effect of a cleaner ladder on the path from amateur to professional.

In both VALORANT and League of Legends, academies and professional teams use high-tier ranked performance as one signal for talent discovery. A player reaching Challenger or the highest tiers may attract scout attention. In many cases, this is the only path for a player without a network to access professional opportunity.

If a significant share of high-tier accounts are climbed by others, that signal is contaminated. Scouts cannot distinguish between an account that reached high tier through the owner's real skill and one that reached it through a boosting service.

An effective Anti-Boost system improves this signal quality, and therefore has indirect value for the scouting path. This is a benefit the report itself does not mention, but it is an important part of the overall picture.

I mark this as a medium-probability, long-horizon effect. It cannot be quantified from existing data, but it has clear structural logic.

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Regional factors: A gap in published data

Another notable point about published data: there is no regional breakdown at all.

The 296,416 figure is not split by country or region. This is a meaningful gap.

Boosting demand is not evenly distributed globally. It tends to concentrate in markets where rank has high social value, where the account market is strongly monetised, and where there is a large gap between skill supply and demand.

If Riot published a regional breakdown, the community would have a clearer map of where the problem concentrates. That could help analysts better understand the boosting market's economic dynamics, and help regional managers allocate resources better.

The absence of this breakdown may be a strategic choice, or a report limitation. There is no way to determine from existing data. But it is a point I will track in subsequent reports.

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The counter-angle: Does crackdown harder actually work harder?

The default community assumption is that tighter enforcement reduces cheating. This seems obvious. But the data in the report does not prove it.

There are three explanations for a large enforcement figure, and all three are consistent with the data.

Explanation one: the system is detecting better, and the large figure reflects improved effectiveness. This is the explanation the publisher wants the community to believe.

Explanation two: the problem is bigger, and the large figure reflects an actual increase in cheating activity. In this case, the large figure signals failure, not success.

Explanation three: both are happening simultaneously. Detection is improving, but cheating activity is growing faster, and the net result is a larger figure reflecting both trends.

These three explanations lead to three different policy conclusions. Explanation one suggests continued investment in detection. Explanation two suggests changing the market's economic incentives. Explanation three suggests both.

With existing data, they cannot be distinguished. This is why demanding a baseline is not a technical quibble. It is a substantive requirement for policy evaluation.

If subsequent reports publish the figure as a time series, and if the figure declines, that will be better evidence of effectiveness. If it keeps rising, the question of whether enforcement keeps pace with evasion becomes more urgent.

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What happens next: Signals to track

I close the analysis section with a list of signals to track in coming quarters. This is how I handle every topic I follow, whether a football transfer window or an enforcement cycle in esports.

Signal one is Riot's next enforcement report. If it publishes a new cumulative figure with a baseline, trend analysis becomes feasible. If it continues to publish a single figure without comparison, analytical limits remain.

Signal two is any high-profile false positive. Such a case would test the credibility of the intent-based standard and could force Riot to clarify the appeals mechanism.

Signal three is any clarification on joint liability. If Riot publishes a pairing threshold or a clearer distinguishing criterion, false-positive risk falls. If not, the risk continues.

Signal four is boosters' adaptation. If new violation categories are added to the enforcement taxonomy, it shows the arms race continues and adaptation is underway.

Signal five is comparison with other publishers. If a competing title publishes comparable data, it will provide context for assessing Riot's scale claim.

These five signals form a tracking system. They do not give me immediate answers. They give me a framework to ask the right questions when new data arrives.

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A methodological lesson: Reading a statement as a data table

I want to close with a thought on methodology, because this is the part that applies more broadly than the specific topic.

When I read a publisher's enforcement statement, I apply the same process I apply when reading a match report.

First, I separate fact from interpretation. The 296,416 figure is fact. The claim "cracking down harder" is interpretation. Both appear in the same text, but they have different evidential value.

Second, I identify the baseline. No baseline, no trend. No trend, no evaluation of policy effectiveness.

Third, I identify who controls the data. Self-reported data has different value from independently audited data.

Fourth, I identify what is not said. In this case, the appeals mechanism is not described, the liability threshold is not stated, the regional breakdown is not published. These gaps matter as much as what is presented.

Fifth, I identify stakeholders and their incentives. The publisher has an incentive to present its system favourably. The community has an incentive to demand fairness. Violators have an incentive to adapt and evade.

This process is not complicated. It only demands discipline. And in analysis, discipline matters more than intuition.

Russia taught me that the crowd and the data always tell two different stories.

In this case, the crowd says Riot is winning the war on boosting. The data cannot yet say that. The data only says Riot actioned a large number of accounts over an undefined period, through a system where it controls both detection and adjudication.

That is an important difference. And like every important difference, it lies in what is not said.

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Forward thought: Integrity is a process, not a state

What I take from this analysis is not a conclusion about whether Riot is right or wrong. It is a way of seeing integrity in online competition.

Integrity is not a state achieved once and maintained. It is a continuous process in which stakeholders adjust to one another. The publisher improves detection. Violators improve evasion. The community improves demands. And the cycle repeats.

In such a process, there is no final victory. Only continuous adjustment. The right question is not "has Riot won", but "in which direction is the pace of adjustment heading, and who is ahead".

With existing data, I cannot answer that question. But I know what to track to be able to answer it in coming quarters. And in analysis, knowing what to track is often more valuable than having a ready answer.

An empty stadium is the most perfect laboratory I have ever stepped into.

An online ladder is the same. It is a laboratory of integrity governance, running continuously, with tens of millions of participants, and with data flowing out every second. The remarkable thing is not that the system is perfect. The remarkable thing is that it exists, and that it is being adjusted.

The question I carry into the next quarter is simple: will Riot's next disclosure come with a baseline, or just a larger number? The answer will tell me whether the system is learning, or merely counting.