The 90-Pull Gacha and a Valuation Lesson from the Transfer Room

In June 2026, I submitted a report to the club's board proposing the signing of Lee Kang-in for 8 million euros. They rejected it, arguing the midfielder "could not demonstrate defensive ability." Six months later, Lee Kang-in shone and helped Mallorca survive relegation, while my club finished eighth. Looking back at the entire email chain, the data reports, and the meeting minutes, I understood the problem was not the fee. The problem was the valuation architecture.

That architecture, when I compared it against the banner system of an open-world role-playing game, turned out to be more sophisticated than any transfer market I have ever operated in.

The transfer market has an open and a closed window. The gacha system has versions. Each version splits into two phases of roughly 21 days, each with its own banner. That rhythm is not a match calendar; it is a revenue calendar. What interested me in the analysis was not the name of the new character, but the minimum price of acquiring one.

That threshold is 90 pulls. Within 90 pulls, a player is guaranteed a five-star character. This insurance mechanism is called pity. But the first five-star has only a 50% chance of being the banner's featured character; the other 50% falls to the standard pool. If you lose, the next five-star is guaranteed to be the featured character.

Set beside the transfer market, two structures look remarkably alike. The 90-pull pity is a release clause: a ceiling at which all negotiation stops. The 50/50 system is a contract with a performance-contingent clause.

The difference lies here: pity is shared across banners of the same type. A player who has accumulated pulls on one banner and then switches to another of the same type keeps the insurance count intact.

In a transfer room, that is a clause every sporting director dreams of. You negotiate with Club A, talks collapse, yet all the time and effort already spent carries over intact to negotiations with Club B. Not a cent lost. Not a day lost.

This architecture does not aim to maximize any single transaction. It aims to maximize transaction frequency. By driving the switching cost between same-type banners to near zero, it turns the player from a one-time buyer into a continuous buyer.

Compare that with professional esports, where I work every day: money flows through sponsorship, broadcast rights, in-game skin revenue sharing, and prize pools. There, revenue depends on the appeal of the tournament, on match results, and on whether a season tells a good story. A gacha system depends on none of that. The publisher controls supply, controls price, controls information, and collects money directly from players without a third party.

Then comes the rerun policy. There is no fixed schedule. Some characters are absent for over a year; others return within a few versions.

In the summer of 2026, I spent an entire season measuring things that seemed unmeasurable. People called it a natural experiment. I called it a chance to measure luck. The same principle applies here: scarcity is not random. It is a controlled variable. No fixed rerun schedule means players cannot plan optimally, forcing them to hold premium currency at all times.

Beside that sits a secondary revenue lane: a legacy banner, loosely called Chronicled Wish. It has its own rules and is usually reserved for older characters. Strategically, it re-monetizes assets that have stopped earning, without disrupting the primary banner cadence.

A team that scores penalties in 6 of 6 matches is not playing football; it is playing luck. The gacha system runs on exactly that logic. The difference: in football, luck cannot be controlled. In this system, luck is designed, measured, and sold back to the players themselves.

Specifically, in the transition from 7.0 to 7.1, phase two of 7.0 reruns older characters such as Flins and Ineffa, while phase one of 7.1 releases two new characters at once, Vesna and Vodyanitsa. The peak of financial pressure on players falls exactly in phase one. Players who poured currency into the 7.0 phase-two reruns walk into 7.1 empty-handed. From a market-management standpoint, this is deliberate design, not coincidence.

A transfer fee is the number one person is willing to pay. True value is the number that data does not need to negotiate. In the transfer market, that holds. In the gacha system, it holds even more: what players pay is not the price of the character, but the price of the position they occupy in the emotional supply chain.

Here I have to be blunt.

Reading through the 28 information points in the original analysis, I counted 20 with no source. Only one cites an official publisher source. Three are the author's personal opinion. Several character names and version numbers cannot be cross-checked against the known game state. The analysis itself admits: the exact banner schedule is still unconfirmed.

Do not trust the table; ask xG. The table tells the past; data tells the future. There is no table here, but there is something similar: a banner schedule is not value. Knowing when a character appears does not mean knowing how much that character is worth.

The community is discussing "saving for 7.1." The analysis offers not a single line of data on the power, kit, or combat value of the two new characters. They know "when," not "why." This is schedule-driven content, not decision-support content.

I have been in exactly that position. In June 2026, analyzing a World Cup match, I used pressing data to argue against the majority. The piece was attacked. Three weeks later, an official report confirmed what I had said. The risk of going against the crowd is real. The risk of following the crowd without verification is greater.

The crowd hypes because of the schedule. I wait for data on value.

Twelve years of watching the industry taught me the lesson is not in character names or version numbers.

A monetization system built entirely on direct in-product spending, dependent on no sponsorship, no broadcast rights, and no tournaments, will weather calendar shocks better. In return, it exposes itself to a different risk: rules on probability transparency and consumer protection.

I started from a student blog with 2,000 views. Data does not care who you are; it only cares whether you read it correctly. And the question for the next cycle is not which character is stronger, but this: when the publisher is simultaneously the rule-maker, the information authority, and the money collector, who will step up to verify that 90-pull threshold?