The Empty Spreadsheet: Where Sports Hides Its Unmeasured Losses

The Empty Spreadsheet: Where Sports Hides Its Unmeasured Losses

On a Friday morning in late September 2026, on the third floor of Incheon United's headquarters, I opened a four-page scouting file. The first page carried a player's name, a 19-year-old from a lower-division club. The next three pages were blank. The top-speed field was empty. The successful-duels field was empty. The scout's notes field was empty. Only one line at the bottom of page four, handwritten in blue ink: "Not enough viewing."

In the club's archive system, that file was marked complete. Nobody flagged an error. Four months later, when I reopened it, the software still displayed it as an ordinary report — just a report with a lot of empty cells. And in the March 2026 scouting meeting, when someone asked about the player, the answer came back in two seconds: "No data, skip him."

That was the moment I began to distrust the entire way the sports industry reads missing data. An empty cell in a spreadsheet does not mean "this player is poor." It means "nobody has measured yet." Those are two entirely different statements, and the gap between them is where money disappears.

Based on my experience watching matches in the K-League, the AFC Champions League and every World Cup from 2026 to 2026, I would argue the most expensive mistake in professional sport is not mispricing a player. It is mispricing a gap.

An empty cell gets read as a zero

This industry runs on three document types: scouting reports, financial reports and commercial reports. All three share one disease, and the disease does not live in the wrong number. It lives in the absent number.

When a sporting director receives a report on a young midfielder, he looks at the "key passes" column. If it reads 0, he concludes the player is not creative. If it is blank, he concludes exactly the same thing. Cognitively, those two states collapse into one. Statistically, they belong to different worlds: one is a failed observation, the other is an observation that never existed.

I once built a small Excel model to test this. I took scouting data for 42 academy players at Incheon United in the 2026 season. Of the 42 files, nine had at least 30 percent of their fields left blank. When I asked three independent scouts to re-evaluate those nine files, all three placed them in the "high risk" bucket — even though none of them had ever watched those players live. The gap itself had become a verdict.

The psychology here is simple. The human brain hates ambiguity, so it automatically fills empty cells with negative assumptions. A data-poor file looks like a bad file, because neither has anything to argue with. And in an industry where decisions must be made before a deadline, the negative assumption is the safe choice for the individual decision-maker — even when it is the ruinous choice for the club.

That is why I started calling these gaps "the dead zones of the spreadsheet." They never appear in the year-end financial report. They never appear in the press conference. But they decide who gets signed, who gets dropped, and which club pays the price three years later.

The 2026 file: a player with no price

Back to Incheon United in 2026. I was 29, working as a mid-level financial analyst. I was handed a task that was not in my job description: build a player valuation model combining on-pitch performance metrics with Instagram follower growth rates.

The idea sounded eccentric to traditional football people, and the board reacted exactly as I expected. They called it "a fan game." But I had a specific reason for doing it, and that reason began with an empty cell.

In the club's commercial dataset there was a column labelled "estimated commercial value." It was filled in by hand, once a season, by a marketing staffer. Most of the cells in that column contained the same number: the player's current contract value. In other words, the club was valuing its commercial assets at their own accounting cost. That "commercial value" column was, in substance, an empty cell in disguise.

I built the model differently. I took the 214 percent Instagram follower growth over six months of a 23-year-old midfielder named Kim Do-hyuk, placed it next to his performance metrics, and benchmarked him against a group of 30 K-League players in the same position. The result: Kim's on-pitch performance index sat in the middle of the pack, but his digital engagement growth ran three times the rate of the top-performing group. He was an underpriced asset, and nobody on the coaching staff knew it, because nobody was paid to know it.

Management rejected the proposal. Not because they disputed the data. They rejected it because the data came from a source outside the standard workflow. A report with no precedent gets treated like a report with no data.

I wrote the report anyway, and developed three parallel model versions: one using Instagram data alone, one combining digital data with performance metrics, and one using performance data only but adding a "actual minutes played" variable. All three reached the same conclusion by three different routes. That is the principle I still hold: if a conclusion only survives on one model, it is not yet a conclusion. Players do not have prices — they have stories, and the market does not know how to read them.

Six months later, another K-League club signed Kim Do-hyuk for a negligible fee. The following season, he ranked among the ten most digitally engaged players in the league. The commercial value Incheon United forfeited was never entered into any report, because a missed opportunity is not an accounting line item. It is the largest invisible loss in this industry.

The 2026 file: broadcasting revenue looks best when you don't ask where it came from

In 2026, aged 30, I was assigned by the CEO to monitor the Korean Football Association's sponsorship performance during the World Cup in Russia. I tracked the Korea versus Mexico match on 23 June 2026, which finished 1–2. Son Heung-min scored a late consolation goal, and the whole country was glued to its screens.

The numbers I collected were impressive: 4.2 million online views. But when I pulled one more row into the spreadsheet — shirt sales revenue across the two weeks around that match — the result inverted completely: down 17 percent year on year.

That paradox took me three weeks to explain, and during those three weeks I argued with the communications department more than in the rest of my career combined. My conclusion: the traditional broadcasting rights model is forfeiting roughly 11 billion won in digital-platform revenue, because rights contracts were designed for broadcast television, where value is measured in households rather than in purchasing behaviour.

My point was never the 11 billion figure. It was that nobody in the supply chain knew that figure existed, because no report contained a cell to record it in. World Cup broadcasting revenue is the prettiest number in sport when you don't ask where it came from — and worse, when you don't ask where it went.

I proposed five digital monetisation experiments, from per-match content bundles to advertising deals tied to individual in-game moments. Two were rejected immediately at the review stage for lacking precedent. The remaining three entered small-scale trials. None ran long enough to prove its financial case.

The lesson I took was not "I was right." The lesson is that reporting structure determines conclusions. When the accounting system has no cell for digital revenue, digital revenue exists as a gap, and that gap gets read as a zero.

The 2026 file: an empty stadium is a laboratory

In 2026, aged 32, the pandemic turned the stands into rows of empty plastic seats. Incheon United forecast a 12 billion won loss in ticket revenue for that season. That number went into the financial report, was accepted by the board, and became a fact nobody questioned.

I questioned it. Not because I thought 12 billion was wrong, but because I thought it was incomplete. A forecast that counts only the revenue lost from tickets, and never the revenue that could be created out of the emptiness itself, is half a forecast.

I convened a working session with six marketing staff. We put four models on the table: virtual advertising on the broadcast feed, per-match camera-angle ticket sales, community crowdfunding, and short-term per-match sponsorship deals. Nobody in the room was allowed to object on the grounds that "we've never done it."

Two models failed within six weeks. The camera-angle ticket model never hit minimum purchase thresholds. The crowdfunding model raised a few hundred million won but its operating costs consumed nearly all of it. I recorded both failures in the internal report with full detail, because a recorded failure is worth more than a forgotten one.

Virtual advertising was different. Within three months it generated 1.5 billion won. Seoul E-Land later copied the model. That 1.5 billion won appeared in no forecast, because it belonged to no existing accounting category.

A club does not need a full stadium to make money. It needs to know what the empty stadium is saying. The 2026 season taught me that a crisis does not create new problems. A crisis exposes business models that died long ago, and the gaps in the balance sheet that existed long before the pandemic.

The 2026 file: one month re-prices a player

In 2026, aged 34, the Qatar World Cup landed mid-season in Europe. That was a rare window, and I had been preparing for it since 2026 by building a network of agents across Africa and Europe.

Senegalese midfielder Ibrahima Ndiaye, 26, shone in the group stage with two goals and one assist in three matches. His parent club in Ligue 2 kept his old valuation, because his contract had been negotiated before the tournament and contained no performance-linked adjustment clause. In their dataset, three World Cup matches were a gap not yet keyed in.

I persuaded Incheon United to sign him on a six-month loan with a 60–40 wage split, the larger share ours. I chose that structure deliberately: if Ndiaye failed, the sunk cost stayed low; if he succeeded, the increased wage sat outside the season's fixed budget.

Ndiaye scored seven goals in the second half of the season. Incheon United survived relegation. And when the loan ended, his French club had re-priced him at several multiples of the original figure.

Every valuation model is wrong. The question is: wrong in whose favour. In Ndiaye's case, the old model was wrong in the French club's favour for six months — they held an asset below market — and wrong in Incheon United's favour in the following six, when we were the only party actively filling the data gap.

The contrarian angle: this industry rewards silence

There is one thing I need to state plainly, because it is the weakness in my own argument across many years. I used to think the problem was clubs' analytical capability. After more than two decades observing this industry, I believe the problem is incentive design.

A scout is judged on the players he recommends successfully. A player he never recommends never appears in his evaluation, even if he might be the only person who ever watched that player. A finance director is judged on forecast accuracy, so he has an incentive to issue narrow, safe forecasts. A sports journalist is judged on readership, so he writes about what happened rather than about what nobody measured.

Nobody in that chain is rewarded for pointing out a gap. Everyone is rewarded for filling it with a fluent story.

I have made the same error myself, and my error has a specific name: the Kim Do-hyuk valuation model of 2026. When I presented the results internally, I framed them as a firm conclusion. I did not say clearly that my model rested on 30 comparison players — a sample far too small to conclude anything about the whole K-League market. I turned a gap inside my own model into an invisible gap.

That lesson changed how I write reports. Today every report I send contains a dedicated section, placed immediately after the conclusions, titled "What we do not know." It lists unmeasured variables, untested assumptions and scenarios that would falsify the conclusion. Initially it was read as a sign of low confidence. Now it is the most carefully read section.

One further point on local context, since I work across both the Vietnamese and Korean markets. How a data gap gets read depends on the decision-making culture. In some places, the silence of the data is understood as a signal to investigate further. In others, it is understood as a signal of safety. The same empty spreadsheet, two opposite conclusions, and the outcome is decided by whoever sits at the head of the table.

The problem is more severe in women's sport. Data on injuries, load cycles and match volume for female athletes is typically collected at lower density, and denser gaps get read as evidence of lower value. Medical confidentiality blinds fans and media, while clubs disclose only the information that protects their asset values. Here the gap is manufactured deliberately, and that is the most dangerous kind of gap there is.

An open conclusion

If you are holding a dataset with empty cells, the first thing to do is not to fill it with an estimated number. The first thing is to label it: not measured. Those two words change the entire subsequent conversation, because they force the decision-maker to say out loud what basis he is deciding on.

Sport will not soon fix the incentive structure that makes silence profitable. But an individual can do one small thing: every time you see an empty cell, ask who benefits if it stays empty. The answer is usually not in the spreadsheet.

And if you are a fan reading a transfer story full of numbers, remember that the printed figures are only the visible part. The submerged part is what nobody bothered to measure, and that is the part that decides where your club stands in May.