Digging Under the Revenue Sediment: How the Gacha Model Exposes the Fragile Boundary Between Esports and Gaming

A piece of content labeled "Esports" flowed into my database this week. When I opened it, the first thing I saw was not a roster, not a bracket, not a PPDA figure, not an age curve. I saw a banner schedule. I saw the number 90 pulls, a 50/50 rate, and a list of character names I had to look up three times to confirm they do not exist in any professional tournament. When the crowd looks up at the bright screen, I dig under the dust of old data. And the dust this time was thicker than I expected.

I tell this story not to catch out any editor. I tell it because that wrong label is a systemic phenomenon. It exposes a problem that anyone doing esports industry analysis will encounter: more and more content about "games" is being mixed with content about "esports", and these two, although both built on the same technology platform, operate on two fundamentally different revenue models. Readers are misled. Analysts are poisoned at the input layer. And the market, as always, pays the price with decisions built on a wrong foundation.

Every prophecy lies in the sediment the crowd rushes past.

The source article I am excavating discusses Genshin Impact, an open-world action RPG published by HoYoverse, and more specifically the character banner schedule for versions 7.0 and 7.1 together with its gacha pity mechanics. Of the 28 information points I extracted, 20 carry a blank source field. Only one cites an official publisher announcement. Three are the author's personal opinion. Several named entities — Odette, Flins, Ineffa, Vesna, Vodyanitsa — along with the version numbers "7.0/7.1" cannot be cross-verified against any known game state, and carry a high risk of being fabricated, speculative, or AI-generated content.

That is my first conclusion. And it forces me to reset the entire analytical framework.

The Wrong Label and Its Cost

Before entering any analytical dimension, I must state something methodological. Genshin Impact has no official professional tournament circuit. It has no equivalent of Worlds, no The International, no Major, no VCT. It has no franchised league, no club ecosystem, no player-transfer market in the esports sense. It also has no competitive balance patch system in the esports sense — its "versions" are PvE content drops, not competitive-balance patches.

The consequence is clear: the nine-dimension esports framework is structurally inapplicable to the bulk of this article. If I tried to force it, I would manufacture false equivalences — treating characters as players, treating banner phases as tournaments. That is the work of a lazy person filling a template, not the work of an archaeologist.

But wait. Before discarding, I need to establish exactly what retains value. And it turns out, something does. The gacha model the article describes is a clean, teachable example of monetization design — a model adjacent to, but fundamentally different from, the esports monetization model. That comparison is what is worth writing about.

I do not drill into the moment; I drill into the sedimentation process of a talent. And this time, the "talent" I am excavating is a revenue architecture.

What Gacha Is, and Why It Differs from Esports

Gacha is a monetization mechanic based on randomized rewards exchanged for paid currency. Players spend money or accumulated resources to "pull" and receive a reward they cannot control. Conceptually it sits next to loot-box debates, and is legally distinct from gambling in most current jurisdictions — but always sits in the neighborhood of regulatory discussion.

In esports, revenue comes from a completely different structure. Sponsorship. Broadcast rights. Revenue share from in-game skins. Prize pools. Live ticketing. And transfer deals. These four pillars — sponsorship, broadcast, digital goods, and prize money — form a value chain in which clubs, players, organizers, and publishers split revenue. This model depends on audience attention, and therefore on the calendar, on results, on drama, on the season.

The gacha model is different. It does not need an audience. It needs spenders. Its value chain is a closed loop: the publisher creates content, the publisher sets the pull rules, the publisher announces the schedule, and players spend directly in-game. There is no club intermediary. No broadcast layer. No independent tournament organizer. The publisher is simultaneously producer, referee, and beneficiary.

I have written before about the concentration of power in esports ecosystems, where the publisher sets the rules, owns the tournament, and sells in-game items. But there, clubs still exist, players still exist, audiences still exist, stakeholders with opposing interests can negotiate. In the gacha model, that concentration is nearly absolute. That is the first and most important difference.

Anatomy of the Pity Mechanic: the 90-Pull Threshold

The source article provides one notable technical detail: a five-star character is guaranteed within 90 pulls. This is the "pity" mechanic — a safeguard threshold.

I want to pause here, because this is one of the rare pieces of analysable data in the whole article. The number 90 serves a dual function. First, it creates a "floor" — an upper bound for the most pessimistic spending scenario. Players know that however unlucky they are, they need not exceed 90 pulls for one five-star character. This reduces the sense of infinite risk, and therefore lowers the psychological barrier to starting to spend. Second, it creates an intermediate goal — players can count down. Each failed pull is a step closer to a guaranteed reward. This psychological game turns spending behavior into a journey with an endpoint, rather than a bottomless gamble.

People call it luck; I call it having finished reading three years of baseline data.

In esports, we have no equivalent mechanism. There is no "floor" guaranteeing that a club will certainly get a star player after spending a certain amount. The transfer market is a negotiation market, where price depends on form, contracts, supply and demand, and relationships. Risk there is real risk — buy an expensive player and he may get injured, fail to integrate, decline. The gacha model removes that risk, or at least contains it, and that is why it monetizes so effectively.

The 50/50 Mechanic and the Architecture of Revenue Variance

The second point the article reveals is the 50/50 system. On an event banner, the first five-star has a 50% chance of being the featured character and a 50% chance of a standard character. If the result is a standard character, the next five-star is guaranteed to be the featured one.

This is a sophisticated monetization design. It creates two types of experience. For the lucky, they get the desired character at low cost, feel satisfied, feel attached. For the unlucky, they must spend more to reach the "guarantee" — but because they have already invested, the sunk-cost psychology keeps them going. The 50/50 mechanic does not lower average total spend. It raises the variance of spend, and in behavioral economics, higher variance often accompanies higher total spend in a segment of users motivated to complete collections.

Place this next to a familiar esports activity: selling digital items. A skin is listed at a fixed price. The fan decides to buy or not. No variance. No guarantee. No psychological pull other than whether the item itself is appealing. This is why digital-goods revenue in esports is more stable and predictable, but its ceiling is also theoretically lower. The gacha model trades predictability for a higher revenue ceiling — a deliberate choice.

Guarantee and Sunk-Cost Psychology

I want to dig deeper into the guarantee mechanic, because it is the final piece of the psychological architecture. After a first failure, players know the next five-star is certainly the target character. "Certainly" is a strong word. It turns a probabilistic behavior into a deterministic one. And when a choice becomes deterministic, the human brain tends to value it more highly than a probabilistic choice with the same mathematical expectation.

In esports analysis, I often have to be careful with claims like "certain". Nothing is certain on the pitch. A strong team can lose to a weak one. A young player can explode or collapse. That very uncertainty is what makes sport compelling. The gacha model does the opposite: it sells uncertainty away, but sells it only to those who have already paid. It is a way of monetizing by reducing the sense of uncertainty for paying customers, while still maintaining enough randomness to create scarcity and desire.

There is no miracle on the pitch, only fragments assembled before others can see them.

Shared Pity: a Revenue-Smoothing Mechanism

A fourth technical detail the article notes: pity is shared across banner types of the same category. This means a player's pull progress on one banner carries over to another within the same type.

The economic consequence is clear. If pity were not shared, players would hesitate to switch from one banner to another, since each switch means starting over. When pity is shared, the switching barrier disappears. The marginal cost of switching banners falls. And when marginal cost falls, spending frequency rises.

This is a revenue-smoothing mechanism. It makes cash flow not only concentrated around new-character launches, but flowing evenly across reruns of old characters. The publisher gains stability. Players gain the feeling that their effort is not wasted when their strategy changes.

In the esports transfer market, we have a mechanism similar in logic but different in form: contract clauses, release fees, and loan-transfer mechanisms. They too aim to make talent flows more flexible, reducing transaction barriers. But they operate in a market with many negotiating parties, whereas shared pity is a rule unilaterally set by one party. The power difference is absolute.

The Two-Phase Rhythm and the Design of Purchase Windows

The article states that each version is split into two phases of roughly 21 days, each with its own banner. This is a rhythm, and rhythm in business is a tool.

I have spent years observing esports match calendars, and I noticed one thing: the calendar does not only serve competition. It creates an emotional rhythm for the audience. This week there is a classic match, next week a break, the week after that the knockout stage. That rhythm keeps attention from cooling. The gacha model's 21-day two-phase rhythm operates on the same principle, but the goal is not attention — it is spending. Every 21 days is a new decision window. The old window closes, a new one opens. Players are never in a state of complete calm; there is always a new deadline approaching.

21 days is also a psychologically interesting number. It is long enough for players to accumulate free resources, but not long enough to accumulate enough for everything they want. The result is a predictable gap between desire and affordability — and that gap is where revenue is born. If the window were shorter, players would give up as infeasible. If longer, they would accumulate enough and spend less. The number 21 is a finely tuned equilibrium point, though I cannot confirm whether it results from controlled testing or mere tradition.

Scarcity Without a Fixed Schedule

Another key feature: the rerun schedule is not fixed. Some characters are absent for over a year, others return after just a few versions. The article notes this as a fact, without explaining the mechanism.

I will explain it. This is a controlled scarcity mechanism. In economics, when the supply of a good is unpredictable, consumers tend to value it more highly and act faster when it appears. Uncertainty about timing creates FOMO pressure — the fear of missing out. If the rerun schedule were fixed, players could plan long-term and spend with discipline. When the schedule is not fixed, long-term planning becomes infeasible, and players must react to each announcement.

This is a fundamental difference from esports. The esports calendar is published months, even a year in advance. Audiences know when Worlds takes place. They can plan. That stability is part of the product's value. The gacha model treats schedule uncertainty as an asset, not a flaw. It turns information into an instrument of power.

An empty pitch is not a stopping point, but a new stratum to excavate.

Chronicled Wish as a Secondary Revenue Lane

The article mentions a mechanic called Chronicled Wish, a separate banner with its own rules, typically for older characters. This may be the most strategically important detail in the entire article, even though it is only mentioned in passing.

I read it as a secondary revenue lane. Imagine a publisher with hundreds of characters in stock. New characters need to be promoted on the main banner to maximize launch revenue. But old characters still have fans, still have people who want them. If you put them on the main banner, you must sacrifice a slot for a new character. Chronicled Wish solves this by creating a separate channel for old characters that does not occupy a main-banner slot.

This is an asset-portfolio management design. It allows the publisher to re-monetize characters past their peak without diluting attention for new ones. In esports, we see a similar logic in a different form: anniversary tournaments, exhibition matches between retired players, nostalgic events. They re-monetize the value of talents who have retired or passed their peak. But they operate at the event layer, whereas Chronicled Wish operates at the direct, continuous revenue layer, requiring no special occasion.

In the darkness of old tactics, I found the fossil of a playstyle not yet born. And in this secondary revenue lane, I see a model that esports organizations could learn from, or should be wary of.

The Publisher as Both Referee and Beneficiary

The only officially sourced information point in the source article is a publisher announcement. This, together with the pity-rule structure, exposes a governance problem I have warned about repeatedly in esports analysis.

The publisher is simultaneously the game operator, the setter of pull rules, and the announcing authority. There is no independent referee. No independent verification body for disclosures. When one entity writes the rules, enforces the rules, publishes the results, and profits from those rules, you have a nearly absolute concentration of power.

I have written about a similar tension in esports, where the publisher owns the game, runs the tournament, and sells in-game items. But in esports, opposing-interest stakeholders always exist: clubs have associations, players have unions, broadcasters have contracts, sponsors have contracts. These create checks and balances. In the gacha model, such checks are nearly absent. Players are a dispersed collective, with no negotiating organization, no collective voice in shaping pull rules.

The Source-Reliability Problem: 20 of 28 Points Unsourced

I must speak plainly about the quality of the source article, because it is the foundation of any analysis.

Of the 28 information points, 20 carry a blank source field. Only one cites an official source. Three are personal opinion. Named entities cannot be cross-verified. The versions mentioned do not match any known game state I can look up.

For a data archaeologist, this is the sign of a disturbed stratum. It does not necessarily mean everything in the article is false. It means I cannot distinguish which parts are true and which are false, and therefore I cannot build any solid conclusion on top of it.

There is one positive signal: the article itself admits its uncertainty. It notes that the exact banner schedule is still unconfirmed. This is an honest editorial act, and I acknowledge it. But admitting uncertainty is not the same as providing reliable information. It only means the author knows he is standing on weak ground.

The greatest risk to a reader acting on this article is acting on a false schedule. Meanwhile, the greatest risk to an analyst is drawing conclusions from unverifiable data. Both are traps I try to avoid by drawing a clear line between what I know and what I suspect.

The "Traffic Filter" Culture

There is a phenomenon I have observed over years working between the Vietnamese and Chinese markets: the rise of content designed to attract traffic rather than to provide information.

The source article bears the marks of this content type. A promotional tone — "an exciting adventure" — accompanies a thin sourcing base. A large share of information is unverifiable. Some entities appear not to exist. The goal of this content type is not to help readers understand something true, but to make them click, stay, and share.

This is a systemic problem, and it affects the esports field too. I have seen "analyses" written by people who have never watched a full match, "predictions" created to optimize engagement rather than accuracy. In an information market where traffic is rewarded, truth becomes a secondary variable. And when truth becomes secondary, readers lose the ability to distinguish analysis from advertising.

I do not write to judge. I write to mark a stratum. Content of this kind will keep proliferating, and the only defense is building immunity — the ability to recognize the signs of unsourced content, of unverifiable entities, of promotional prose dressed as analysis.

Gacha Regulation and the Gray Zone of Loot Boxes

One analytical dimension the article does not cover but which matters most long-term: the regulatory framework for gacha and loot boxes.

In many jurisdictions, randomized reward mechanics exchanged for real money must comply with probability-disclosure requirements and minor-protection rules. These regulations aim to ensure players know the true probability of receiving a reward, and that minors are protected from addictive and overspending mechanics. The pity rules the article describes — the 90-pull threshold, the 50/50 mechanic, guarantee, shared pity — mirror mandatory probability-transparency requirements common in some jurisdictions, but the article cites no regulator.

This matters for two reasons. First, it shows the gacha model does not exist in a legal vacuum. It exists in a tightly regulated zone, where changes to probability-disclosure law or minor-protection law can fundamentally change how the model operates. Second, it creates a risk that the esports model faces less. Esports depends on audiences and sponsorship, and the regulations affecting it are mainly about competitive fairness, betting, and image rights. Gacha depends on direct spending, and is therefore more exposed to consumer-protection, gambling, and addictive-design regulation.

The gacha model may be more resilient than esports to calendar shocks — it needs no season, no final, no audience. But it is more exposed to regulatory shocks — a change in the law in one major market could force a complete redesign of the entire pity and disclosure system.

Comparing Two Markets: a Note from the Workplace

I live and work in Shenzhen, but I was born and raised in Vietnam. This position gives me a vantage point that those standing inside only one system rarely have.

In China, where I track youth academies and development systems, I see a sophistication in product design and revenue design that is hard to find elsewhere. Gacha mechanics are fine-tuned to the percentage point. Schedules are calculated to maximize revenue while maintaining user satisfaction. In Vietnam, where I still track youth systems and esports organizations, I see a different approach — closer to the audience, more dependent on community and sponsorship, and less sophisticated in revenue design.

This comparison is not to say which is better. It is to point out that the gacha model is a product of a specific industrial context, and applying it to everything is a methodological error. When an article about gacha is labeled "Esports" and flows into my database, I do not just see an editorial error. I see a tendency to treat everything related to games as esports, and that tendency destroys the ability of an entire industry to analyze accurately.

An academy does not produce stars; it only preserves the fingerprints of fate. And revenue models are the same — they do not create value from nothing, they only preserve and amplify human behavior. Understanding that behavior is understanding both the revenue and the risk.

Lessons for the Esports Analyst

So what do I take from this excavation?

First, the boundary between esports and the wider gaming industry is eroding, and analysts must actively rebuild it. Esports is an organized competitive structure with clubs, players, audiences, and seasons. The gaming industry is a product and revenue structure that may include esports but is not synonymous with it. When we conflate the two, we lose the ability to analyze either.

Second, the revenue model exposes an ecosystem's risks better than any claim about growth. An ecosystem dependent on audiences carries different risks from one dependent on direct spending. An ecosystem with many negotiating parties carries different risks from one controlled by a single party. Looking at revenue structure is looking at the spine of the whole system.

Third, source quality is an unavoidable variable. In an information market where traffic is rewarded, unsourced content will keep being produced and spread. Analysts must build a verification discipline before feeding any data into their models. An unverifiable information point is not a weak information point — it is a toxic one, because it can infect every conclusion built on it.

Fourth, and perhaps most important, I learned that carefully noting what is unknown is as important as noting what is known. The source article admits its uncertainty. That is an honest act, but it is not enough. A good analysis must do more than admit it is unsure. It must state clearly what is certain, what is not, and why that line exists. That is the difference between a piece that admits uncertainty and a piece that masters it.

Takeaway

As I write this, I am still keeping the list of unverifiable entities — Odette, Flins, Ineffa, Vesna, Vodyanitsa — in a separate file, marked "awaiting official confirmation". I do not delete them, and I do not use them. I keep them as a reminder that in data archaeology, most of the time is spent between those two states: not yet affirmable, but not yet dismissible.

If you are a reader looking for information about a game, ask whether the piece you are reading has sources, whether its entities are verifiable, and whether its tone aims to inform you or to keep you. If you are an analyst building models of the esports industry, take the time to reclassify your input data. A wrong label may not cause immediate harm. But over time, it will quietly shape every conclusion you draw, in ways you may not notice until it is too late.

The question I leave for myself, and for anyone reading this far, is not "which model is better". The question is: when a system writes the rules, enforces them, announces them, and profits from them, what do the rest of the people in that system use to check and balance? And whether in esports, where the concentration of power is also increasingly rising, we are entering the neighborhood of that answer.