Anti-Boost: 296,416 Accounts and the Fight to Preserve Ranked Integrity in Both VALORANT and League of Legends

3 a.m. Seoul time. I sat in front of two monitors, tracking a VALORANT ranked match while simultaneously keeping a League of Legends ranked tracker open on the second screen. Both accounts were climbing at a speed that made me stop. One account went from Platinum to Diamond in 46 hours. Another climbed from Gold to Platinum in a single night. What caught my attention was not the speed. What caught my attention was the mechanics. The player behind those accounts did not move like someone at his supposed rank. He moved like someone playing two or three tiers below his actual skill.

I logged every match. I logged minutes played, kill counts, assist metrics, win rates, and climbing velocity. After two weeks of tracking, I had a list of more than 200 accounts showing similar patterns. That was the first time I realized the work I was doing was no longer purely competitive-data analysis. I was touching a different part of the esports industry — not the part on the official stage, but a deeper layer: the online ranked ladder.

And that was exactly when the information about Riot Games' Anti-Boost surfaced. Anti-Boost is Riot Games' automated enforcement system, designed to detect and penalize rank manipulation — including boosting, account buying and selling, intentional deranking, and smurf-assisted climbing. This is not a champion balance patch. This is not a tournament-format change. This is a governance statement. And it deserves serious analytical treatment.

According to what Riot Games published, the Anti-Boost system has processed 296,416 accounts showing rank-manipulation behavior across both VALORANT and League of Legends. That figure is presented as a cumulative total, with no split by title, no split by region, and no baseline for comparison to a prior period. When the numbers do not lie, my heart begins to listen. But when the numbers are incomplete, I have to read them with a different attitude — the attitude of someone long accustomed to distinguishing real data from presented data.

I am not writing this to conclude that Riot is doing well or is doing poorly. I am writing this to place that system inside an analytical framework I can reuse. Because boosting is not a new phenomenon. It is a gray-market economy with structure, with supply, with demand, with prices, with risk, and with a recidivism rate. And every gray-market economy can be measured — if we choose the right variables.

The context here matters more than it appears. Over the past five years, I have tracked the movement of the entire online ranked ecosystem. The ladder is not merely where players entertain themselves. It is the input to a much larger value chain. Academies scout high ranks to find young talent. Professional teams look at climbing metrics to assess potential. Sponsors look at daily active players to price contracts. And betting platforms look at the transparency of ranked match outcomes to build derivative markets.

When an account is boosted, it does not simply ruin the experience of nine other players in one match. It corrupts a chain of signals. It injects a false data point into the system — a player who appears to be at a high level but is not. And if that false data point is multiplied hundreds of thousands of times, the scouting system begins to misread the talent map of an entire region.

I once took part in a young-talent evaluation project for a small esports organization in Seoul. We used ranked placement as a primary filter. We screened accounts sitting in the top 0.5 percent of the server. Then we reviewed their matches. In one cycle, we discovered that nearly 12 percent of the accounts on our shortlist showed signs of boosting. Not all were clear-cut. But enough that we had to rewrite our entire screening process.

That is why I care about Anti-Boost. Not because I want to argue with Riot. But because I want to understand whether this system actually produces a cleaner ladder.

Now let us go into the core. I will dissect the Anti-Boost system into the four penalty tiers Riot published, along with an extended-liability model that I consider the most sensitive point of the entire policy.

The first tier is detection and restoration. When the system detects an account engaging in rank manipulation, Riot cancels all ranked points and rewards gained from the cheating behavior. The account is returned to its original rank before the interference. Additionally, the account may be temporarily suspended. This is the most common tier of handling, and it is restorative rather than punitive in nature. It is closer to erasing a stain from a scoreboard than to ejecting a player from the field.

The second tier is escalation. If an account reoffends, the ban duration increases with each violation. This is an important design, because it acknowledges a reality: the recidivism rate is non-zero. If the recidivism rate were zero, an escalating system would be meaningless. The fact that Riot built an escalating penalty ladder shows they assume a portion of players will return to their old behavior after a first enforcement.

The third tier is severe punishment. For account buying and selling or intentional deranking, the penalty can reach a permanent ban. This is the tier applied to the most clearly commercial behaviors. Account trading is a transaction involving money. Intentional deranking is often a preparatory step for another transaction — either to create a low-rank account for resale, or to enable boosting at a lower tier. Riot is striking at the wallet of the gray-market economy, and that is a reasonable choice.

The fourth tier — and this is the one that made me pause the longest — is joint liability. According to the description, not only is the directly manipulated account handled; the booster's main account and even teammates who frequently queue with them may also be actioned. This is a significant expansion of scope. It turns an individual act into a collective risk.

I understand the logic behind this decision. If you only ban the boosted account, the booster continues to exist on his main account. He loses only an alt, which he can recreate in hours. By targeting the main account, Riot raises the true cost of boosting. And by targeting frequent teammates, Riot creates social pressure — a form of peer monitoring inside the system.

But at the same time, this is also the point most prone to error. Imagine an ordinary player who has a friend he queues with often. That friend, for whatever reason, engages in boosting. The ordinary player does not know. They simply queue together like any other friends. If the system acts on queue frequency, that ordinary player could be swept in without any described appeal mechanism.

I have seen similar cases in other anti-cheat systems. When a system shifts from individual handling to group handling, it always trades some precision for deterrent efficiency. And the precision lost tends to fall on the players least able to protect themselves.

What is interesting is that Riot is very careful at another point. They state clearly that a player creating and operating their own alt account is normal activity. Anti-Boost does not target the existence of alt accounts. It targets the intent to manipulate rank. This is an intent-based standard, not a surface-behavior standard. And an intent-based standard is always harder to enforce transparently than a bright-line rule.

When a rule is drawn with a clear line — for example, banning alt-account use entirely — both players and the system know exactly where the boundary lies. When a rule is defined by intent, both sides must interpret. And whenever there is interpretation, there is dispute.

In my world, luck is only the unexplained residual. And in this case, that residual is the probability of a player being wrongly actioned. I am not saying it happens often. I am saying the system is designed to accept a certain error rate, and that rate is not published.

Let us talk about the data dimension. The figure of 296,416 accounts is a large number. But it is a self-reported number. Riot is simultaneously the detector, the adjudicator, and the publisher. No independent third party audits it. This is not an accusation. It is a structural feature of every publisher-run governance system. But when I analyze data, I have to classify sources. And a self-reported figure without independent audit sits in the medium-confidence category, not high.

More importantly, this figure has no baseline. It is a cumulative total. With a cumulative total, I cannot say whether boosting activity is rising or falling. I only know that a large number of accounts were actioned over an unspecified period. If I read this figure as proof that 'Riot is tightening the screws,' I am adding meaning to the data that the data does not contain.

This is a common analytical error. A large number creates a feeling of a trend. But a large number is not a trend. A trend needs at least two data points within the same measurement frame. And here, we have only one.

I have counted every gap on the pitch when the crowd disappeared. In football, I learned that what matters is not only what is counted, but also what is not counted. In the Anti-Boost case, what is not counted includes: the number of wrongfully actioned accounts, the number of successful appeals, the actual recidivism rate, the average time from behavior to detection, and the distribution of behavior by region and by title.

Those are the data gaps. And I am always more interested in the gaps than in what has been filled.

Now let us talk about a point few people notice. Riot pools VALORANT and League of Legends into a single enforcement report. From a governance standpoint, this makes sense — same publisher, same system, same philosophy. But from an analytical standpoint, pooling two games with different economic structures obscures their distinct dynamics.

VALORANT is a tactical shooter. League of Legends is a multiplayer online battle arena. Rank-climbing pressure, boosting demand, and the organization of the account market differ between the two. In a tactical shooter, individual skill can have a larger impact in certain situations, making a boosted account harder to detect at some tiers. In a battle arena, a player's impact on match outcomes depends more on team coordination, making behavior-based detection models differ.

When Riot reports a pooled figure, it hides these differences. And these differences are exactly where I want to dig deepest.

I understand why they do it. A pooled figure is bigger, more impressive, and easier to communicate. But in my line of work, a pooled figure is always a signal that something is being simplified.

Let us shift to the contrarian angle. Most commentary I read about Anti-Boost focuses on the question: is this system effective? That is a reasonable question. But I think it is the wrong question. The right question is not whether Anti-Boost is effective. The right question is whether its effectiveness is sustainable — in a race where one side is an automated detection system and the other is players with financial incentives to adapt.

This is the core asymmetry. Riot must continuously improve its detection to maintain effectiveness at the current level. Boosters need only change their method once to neutralize one detection layer. In such a race, the defending side must always run faster merely to hold position. This is a structural feature, not a temporary failure.

I have seen this pattern before. In football, as injury-prevention systems improved, players and clubs kept finding ways to optimize around them. When a rule is enacted, a market emerges to circumvent it. This is system behavior, not individual behavior.

What Riot acknowledges — that they are improving match-level detection based on behavioral signals — is itself a confession that current methods are imperfect. If they were perfect, you would not need to improve them.

And here is the second contrarian point I want to make. Some analysts argue that Riot publishing a large figure is a strong signal of resolve. I read it differently. Publishing a large figure is first and foremost a reputational signal. It shows players and investors that the ladder is being actively managed. But it also sets an expectation. When you publish a number, you create a standard the community will use to measure you in the future.

If the next disclosure shows slower growth, or if a wrongful-action scandal becomes public, the 'tightening the screws' story is easily reversed. This is a trap that has been opened, and it can only be closed by greater transparency, not by a bigger number.

Every goal is a piece; I do not watch football, I decode it. And in esports, every boosted account is a piece placed in the wrong spot of the big picture. The problem is not that piece. The problem is whether we have a system good enough to remove it before it skews the picture.

Let us talk about the dimension I consider least discussed: scouting value. A clean ladder is not merely a scoreboard. It is a recruitment tool. In recent years, more and more professional esports organizations scout from the high-rank environment. This is a major shift from the early days of esports, when players were typically discovered through community tournaments or personal networks.

When scouting relies on the ladder, ladder integrity becomes a multi-million-dollar asset. An account boosted to a high rank does not only affect other players in the match. It can occupy a slot in a scouting session. And all an academy needs is a few errors in its process to fail to find real talent.

This is a transmission effect I call the bottom-layer effect. I see it most clearly in regions with high player density, where the ladder carries large symbolic value and the account market runs strong. In those regions, an unclean ladder drives up scouting costs, because organizations must add a verification layer they previously did not need.

I once spoke with an academy coach in a region with a developed ranked system. He told me he had stopped trusting rank two years earlier. He only used metrics from organized matches, and used rank merely as a very loose initial filter. I asked why. He said he did not have time to verify every account. I understood that answer. Because I had been in the same situation.

I do not believe in inspiration — I believe in the standard error. And the standard error in the online ladder has grown over time. This is not an opinion. It is an observation from the data I collected. When you add a new variable to a measurement system without removing it, the accuracy of the whole system falls. Boosting is such a variable.

Now let us talk about another dimension I consider important but under-discussed: the link between boosting and other gray markets. In my analysis, I always separate two things: individual behavior and market dynamics. Individual behavior is a player deciding to boost another's account. Market dynamics is the system of transactions that turns that behavior into a priced service.

When a service has a price, it generates a supply chain. Boosters at the top. Account sellers in the middle. Buyers at the bottom. And around them are intermediary platforms, payment channels, and sometimes links to betting activity. Each layer in this chain has its own economic incentive.

When Riot attacks the account-trading layer with permanent bans, it strikes a specific link of the chain. This is a reasonable strategy, because it targets the clearest money flow. But it also has a limitation. If you block one link, the supply chain restructures. Transactions move to harder-to-detect channels. Transactions become more decentralized. And with each restructuring, the detection system must start over.

I consider this a predictable pattern. I have seen it across many industries. There is nothing special about esports. The only special thing is speed. Esports changes faster than most industries, which means the restructuring cycle runs faster too.

Now I want to return to an important governance question. Riot is simultaneously the rule-setter, the violator-detector, the adjudicator, and the publisher of results. No independent appeals body is described. This is a model that concentrates power entirely in the publisher's hands.

I am not saying this model is wrong. In many cases, it is the most efficient model, because it allows fast and consistent response. But it also has a structural weakness. When there is no independent check, the quality of the system depends entirely on the publisher's internal quality. And internal quality cannot be assessed from the outside.

In other industries, when a single entity controls both rule-making and enforcement, checks and balances are typically established. In esports, these mechanisms are not yet mature. And I think this is a gap that needs filling as the industry continues to grow.

Let me offer a specific prediction. I predict that within the next 12 to 24 months, we will see at least one public controversy over wrongful actioning in a major publisher's anti-cheat system. Not necessarily Riot. But a major publisher. The reason is that as automated systems scale, they become more prone to error at the edges — atypical cases, players who do not fit the standard model. And those errors will become public as the community becomes more sensitive to fairness issues.

I make this prediction based on a simple principle. When an automated system scales, its absolute error count rises, even as its relative error rate falls. This is basic math. And it means wrongful actions will certainly occur.

Now let us talk about what I consider most important in this entire analysis. Anti-Boost, ultimately, is an investment in trust. Riot is spending money to protect the integrity of a product. That product is not a specific game. That product is the ranking mechanism — the thing players trust to measure their skill.

When trust in the ranking mechanism erodes, players leave. They do not leave loudly. They simply stop playing ranked matches. They switch to other modes. Daily active players fall. And when daily active players fall, the entire esports ecosystem — from scouting to sponsorship — is affected.

This is why I treat Anti-Boost as something more than an anti-cheat system. It is part of the infrastructure. And infrastructure is always less noticed than what it supports. You do not see a building's foundation. You only see the building.

But when the foundation weakens, the building can still stand for a while. That is the danger. Infrastructure decay always has a lag. You do not see it this quarter. You see it years later, when the numbers have changed too much to go back.

I have seen this happen to other ranked systems in the past. A system starts with a clean ladder. Then small loopholes appear. Then those loopholes are exploited. Then a new generation of players treats those loopholes as normal. And by the time the publisher realizes, they must rebuild from scratch.

This is why I rate Riot's proactive intervention highly. Not because I believe their system is perfect. But because I believe early intervention is always cheaper than rebuilding.

Let us return to the future question. Riot says it will expand Anti-Boost and add match-level behavior-based detection. This is a technically reasonable direction. But it also opens a question about privacy and data. Match-level behavior detection requires analyzing movement patterns, reaction times, and other micro-metrics. This data can be used to improve the system. But it can also be used for other purposes.

This is a topic I think will become more important in the future. As detection systems become more sophisticated, the line between detecting cheating and surveilling players will blur. And when that line blurs, we need a public discussion about where we want to draw it.

I say this not to criticize Riot. I say it because I believe every governance system needs public discussion to maintain its legitimacy. A system that is never discussed becomes disconnected from reality. And a system disconnected from reality loses the trust of those it serves.

Let us talk about the variables to track in the future. I have a list of five variables I will monitor to assess Anti-Boost's real effectiveness.

The first variable is the appearance of a comparative figure. If Riot publishes a new figure in the future, and we can compare it to the current one within the same time frame, we can begin to talk about a trend. This is the most important variable, because it moves us from measuring a state to measuring a process.

The second variable is the appearance of a public wrongful-action controversy. If this happens, it will test the credibility of the intent-based standard. A system is only truly validated when it withstands pressure. And the greatest pressure on a penalty system is community backlash when people feel wronged.

The third variable is Riot's clarification of a queue-with threshold. This is a concrete variable. If Riot publishes a clear threshold — for example, a certain number of queues within a certain time frame — over-reach risk drops. If they leave it undefined, the risk persists.

The fourth variable is booster adaptation. This is the counter-variable. If we see new forms of violation appear, it means the race continues. If we see violation counts fall sustainably, it may mean the system works — or it may mean violators have moved to unmeasured channels.

The fifth variable is cross-publisher comparison. If other publishers begin publishing similar data, we can place Riot's figure in a broader context. A number only has meaning when there is another number to compare it to.

These five variables form a tool. I will not attempt to predict whether Anti-Boost will succeed or fail. That is not my job. My job is to track the variables, and to adjust my model when new data appears.

In this case, my model has a central assumption. That assumption is: in any ranked system, there always exists a minimum rate of accounts with manipulated rank. That rate cannot be zero, because there is always a cost to maintaining a clean system, and always a benefit to manipulating it. The question is not how to drive that rate to zero. The question is how to keep it at a level where the system still functions correctly.

This is a shift in how the question is asked. And I think it is the necessary shift.

Germany left the World Cup not because of Korea, but because of shots that missed the target. In the Anti-Boost case, failure — if it comes — will not come from the system being too weak. It will come from the system failing to measure what it needs to measure. And that cannot be solved by adding more bans. It can only be solved by building a better measurement system.

Now let us talk about a dimension I think will become important in the near future. It is the relationship between Anti-Boost and professional tournament systems. In my analysis, I separate these two fields. One is the online ladder. One is organized tournaments. But these two fields are not entirely separate.

Many professional players come out of the online ladder. They climb, they get discovered, they get invited to tryouts, and they become professionals. If the online ladder is polluted, the scouting system — the gateway into the professional system — is affected.

This is an indirect link, but it is a real one. And I think it will become clearer as more professional organizations rely on ranked data to build their rosters.

Let me give a concrete example. Suppose a team needs to recruit a player for a specific position. They search the high ladder. They find an account with good metrics. They invite that player to a tryout. That player fails to meet expectations. Why? Because that account was boosted, and the actual player does not have skill at that level. This is not a hypothetical. This is a situation I know has happened multiple times.

Each time this happens, that team loses an investment. And over time, they lose faith in ladder-based scouting. When faith is lost, they turn to more expensive methods — tryout camps, youth tournaments, personal networks. This drives scouting costs up. And higher costs mean fewer teams can compete.

This is a transmission effect I call the escalating-cost effect. A small problem at the base layer can lead to a structural change at the top layer. That is how complex systems work.

I spent years analyzing football metrics. And one of the most important lessons I learned is: metrics do not exist in a vacuum. They exist in a context. When you analyze a metric, you must understand the context it was born in. If you do not understand the context, you can reach the wrong conclusion even with correct data.

In the Anti-Boost case, the context is a fast-growing industry where governance systems have not kept pace with growth. This is an important context. It means solutions will not be perfect. It means there will be errors. And it means we need a realistic attitude about what can be achieved in the short term.

But it also means we need a serious attitude about what must be achieved in the long term. Protecting the integrity of a ranked system is not a technical problem. It is an institution-building problem. And institution-building takes time, transparency, and long-term commitment.

I do not know whether Riot has that commitment. I only know they have started. And I know that starting is always the easiest part.

The hardest part is what comes next. The maintenance. The improvement. The facing of feedback, criticism, and edge cases. That is where governance systems are truly tested.

In my world, a system is only validated when it operates under pressure. Any system can perform well under ideal conditions. But ideal conditions are not where systems are used.

So what happens next? I will track. I will log. I will adjust my model as new data appears. And I will wait to see whether Riot's next figure is another cumulative total, or a genuine step forward in measurement.

That is how I work. I do not deliver verdicts. I deliver a framework.

And the question I leave for readers is not whether Anti-Boost is effective. The question I leave is: if you were a scout relying on the ranked ladder, how would you change your process knowing that the 296,416 figure is still only part of the story?

I do not believe in inspiration — I believe in the standard error. And the standard error in the online ladder is something I will keep measuring. Not because I want to prove a point. But because I believe what is not measured cannot be improved.