Empty Analysis: When Sport Makes Decisions on Data That Does Not Exist

The Empty File and the 47 Slides

June 2026. I opened a spreadsheet on an old laptop and found it empty. The forty-seven slides in the presentation were still there — bar charts, trend lines, arrows pointing upward, green cells indicating growth — but the source-data column, the column that was supposed to feed the entire deck, held not a single figure. I had spent six weeks building a sponsorship-effectiveness model for a World Cup campaign, based on data from 64 matches. The model predicted a beer brand would reach 2.1 million impressions. The real figure, after the tournament ended, was 780,000.

But the problem that morning was not the wrong prediction. Wrong predictions I am used to. The problem was that I nearly did not know what I was basing anything on. The deck was ready to send. The client was ready to sign. If my assistant had not accidentally opened the wrong tab and asked a naive question — "where does this number come from?" — a decision worth hundreds of millions of dong would have been made on a foundation that did not exist.

I tell this story not to boast that I escaped. I tell it because in ten years as a sports-marketing consultant in Vietnam, I have witnessed dozens of variations of the same error. An error that is not loud, does not echo, and is never posted online. An error so quiet that it is usually disguised by the very professionalism of the report itself.

Context: An Industry That Lives on Numbers

To understand why this error is dangerous, it must be placed in the proper scale of the industry. Professional tennis today is a vast financial ecosystem, and every decision within it is justified by data. Wimbledon 2026 announced a total prize fund of about 50 million pounds, with the men's and women's singles champions each receiving 2.7 million pounds. The 2026 US Open raised its total prize fund to about 75 million US dollars, with 3.6 million for the champion. The 2026 Australian Open reached a record of about 86.5 million Australian dollars, while Roland Garros 2026 stood at roughly 53.5 million euros. These figures are widely published, verifiable, and they are only the visible part.

The submerged part is where the real decisions happen. A Grand Slam does not only sell tickets and broadcast rights. It sells audience data, logo positions on the net, stadium naming rights, corporate hospitality experiences, and brand presence in the commercial zone. Each such package comes with a quantitative promise: how many impressions, how many broadcast hours, how many viewers aged 25 to 45 with comfortable incomes. And each of those promises must, in turn, be proven with data.

At the national level, the picture is similar. The USTA of the United States operates with hundreds of millions of dollars in annual revenue, most of it from the US Open and its grassroots system. Tennis Australia controls an ecosystem comprising the Australian Open, domestic ATP and WTA events, and a network of academies across the continent. Both organizations publish annual reports detailed enough that every small item can be traced. They do not do this because they love transparency. They do it because sponsors, governments, and member federations demand proof.

Vietnam sits at a different point on that map. Vietnamese tennis has no Grand Slam, no player permanently in the ATP or WTA top 100, and a Vietnam Tennis Federation budget that cannot compare with neighbors like Japan or China. But precisely because the scale is smaller, the margin for error here is more expensive. A wrong investment decision at a small academy can wipe out an entire generation of young players. A failed sponsorship campaign can drive a brand away from the sport for years.

This is why I talk about data, not about stroke technique. Technique is the coaches' concern. But the operating structure — how a decision is born, what numbers feed it, and who bends its direction — is noticed by few. That is the gap in which I work.

Tier One: Where the Data Enters

Every analytical process in sport has three tiers. The first is data entry. The second is interpretation. The third is decision. Errors are born in tier one, concealed in tier two, and become money in tier three. The frightening thing is that tier one is the least checked, because it is the most boring.

I once sat in a meeting where the whole room spent two hours debating ticket-pricing strategy for a tournament, based on a table of attendance data. No one asked where that table came from. I later discovered it had been compiled from an old article online, which in turn cited an estimate with no clear source. An entire decision about ticket prices, audience segments, and VIP seating — all resting on a figure copied three times without anyone verifying it.

In tennis, tier one has specific weak points. The first is score data. Systems such as Hawk-Eye and live-data providers deliver thousands of data points per match: serve speed, ball landing position, first-serve points won, break-point conversion. This data appears objective. But it is objective only when collected correctly, and at smaller events — where full electronic systems are absent — data is often entered by hand by someone sitting at the edge of the court. A manual entry clerk can mistype, can miss a game, can enter the wrong set. And when that data flows into a large database, the error does not vanish. It merely becomes harder to find.

The second is audience data. In Vietnam, measuring the viewership of a tennis match is a vague exercise. Pay television lacks the standardized measurement systems of developed markets. Most viewers watch on online platforms, where view counts can be inflated by many factors: replays, partial views, duplicate accounts. When a sponsor asks "how many people actually watched?", the honest answer is usually "we do not know precisely." But the answer given in the meeting room is usually a specific number, because a specific number sells better than an admission.

The third is financial data. This is the tier I care about most as an operator. The revenue of a club or tournament includes not only ticket and sponsorship money. It also includes the value of in-kind contributions, barter deals, and in-kind commitments converted into cash at some ratio. That conversion ratio is usually chosen by the report's author. And once it is chosen, the final number can be adjusted in the author's favor without anyone calling it fraud, because it all falls within the framework of a "reasonable estimate."

A wrong prediction is not a failure; it is free data for the next calculation. I wrote that line in my notebook after the 2026 World Cup failure. But I must admit one thing: that line is true only when we know where we went wrong. If we do not know what we were basing anything on, the error does not become data. It becomes a blur, and that blur will recur next time, in another project, with someone else in the room.

Tier Two: Where the Data Is Interpreted

Tier two is where errors are concealed. And they are concealed very professionally, because a good interpreter is usually a fluent one. The ability to tell a coherent story from a messy dataset is a skill. But that same skill, when undisciplined, becomes a tool for filling gaps with assumptions that sound very reasonable.

I remember once reading a post-match analysis of a domestic tournament. The analysis concluded that a player lost because of weak mentality at decisive points. The argument was persuasive. But when I checked it against the score data, that player had won most of the important points. The real problem lay in an unusually low first-serve percentage in the second set. The writer had ignored the hard data because it could not tell as gripping a story as the psychology narrative. This is a tier-two error: not wrong about the numbers, but wrong about the weights. People choose the numbers that fit the story they want to tell, instead of letting the story be driven by the numbers.

In sports business, this error is far more common than a purely numerical one. A sponsorship proposal is usually built on a structure: this is a rising sport, this is the fan base, this is the engagement level, this is our proposal. Each proposition is true in some way. But they are selected to create an overall impression, not to describe a reality honestly. The gap between impression and reality is where disappointment is born, usually after the contract is signed.

New media does not kill brands; it exposes brands with no substance. This holds for a player's personal brand as much as for a tournament's. When everything is recorded, when every match has data, when every claim can be checked in minutes, a beautiful but hollow analysis will quickly be recognized. The problem is that it is often recognized too late — after the money has been spent.

Tier Three: Where the Data Becomes Money

Tier three is where decisions are made, and it is where the cost of error becomes tangible. A tier-one error, if caught, costs only time to fix. A tier-two error costs some reputation. But a tier-three error costs real money, and real money cannot be fixed with an explanation.

I once advised an organizer of a regional tennis tournament. They wanted to raise ticket prices for the semifinals on the assumption that demand would be high because the match fell on a weekend. The assumption sounded reasonable. But when I checked the ticket-sales data from the previous three years, I saw something different: weekend matches had higher fill rates, but lower revenue per seat, because most weekend spectators were families buying group tickets at a discount. Raising prices for this audience could reduce total revenue rather than increase it. We decided to keep ticket prices and instead raise the price of corporate hospitality packages, the customer group genuinely more sensitive to seating status than to price.

That decision rested on something very simple: reopening old data and reading it before trusting a new assumption. There was no complex model here. No algorithm. Just the discipline of rereading the source data. And that is what I find most lacking in the industry at this stage.

At club level, tier-three errors often appear as investment in facilities based on a forecast of rising demand. An academy decides to build more courts because it believes enrollment will grow. Where does that belief come from? Usually from a few scattered signals: a small tournament succeeded, an article about the movement, a few parents inquiring. These three signals together can create a sense of a trend, but they do not form a data series. When the new courts are finished and enrollment does not grow as expected, maintenance costs become a burden, and the academy must cut the very investment in coaching — the part that actually creates value.

Here is a paradox I have seen repeat many times: the less data there is, the more readily people invest in tangible things, because the tangible is easier to justify than the intangible. Building a court can be photographed, inaugurated, put in a report. Hiring a good coach cannot be photographed, and results take three years to show. Under limited budgets and short-term performance pressure, the intangible is always cut first. That is a resource-allocation decision, and it is made on gut feeling more than on data.

Vietnamese Tennis and Thin Data Infrastructure

I need to state one thing plainly about the Vietnamese context, because ignoring it would make everything above mere theory. Vietnamese tennis has a thin data infrastructure, and this is no one's fault in particular. It is the consequence of a sport in a formative stage, with resources focused on organizing events and training, not yet on measurement.

The Vietnam Tennis Federation operates the national tournament system, from junior events to national championships. These events generate a considerable volume of data on results, head-to-head history, and each player's progress over time. But most of that data sits scattered across result sheets, organizers' notebooks, and photos of scoreboards. Very little of it is digitized in a way that can be queried and analyzed. When a potential sponsor asks about the three-year development trend of a young player, the answer usually has to be assembled by hand from multiple sources, and its accuracy depends on whether the assembler remembers correctly.

At the international level, the picture is entirely different. The ATP and WTA publish weekly rankings, using a points system based on results over the past 52 weeks, typically counting a player's best results from a set number of events. Grand Slams provide data detailed down to each point. A Vietnamese player entering the qualifying draw of a Grand Slam will appear in the global data system, and every piece of information about that match can be looked up. But the gap between international data on a few individuals and national data on an entire system is very large.

Ly Hoang Nam is the clearest example of both the potential and the limits of this context. He was the first Vietnamese male player to rise to a meaningful position on the professional ATP rankings and to compete in international events at Challenger level and in Grand Slam qualifying. His career generated a volume of international data that Vietnamese tennis had never had before. But when I looked at the youth-training system behind him, I saw an unanswered question: how many other young players are on a similar path, and at what rate are they progressing? There is no standard data table to answer that question. We must estimate, and estimates always carry error.

This is not a criticism. It is a description of a stage. A developing sport usually prioritizes organizing over measuring, because organizing creates events, while measuring only creates reports. But precisely for this reason, those who do analysis in this context must be twice as careful. When the underlying data is thin, every number carries more weight, and every error can be amplified as it passes through multiple tiers of decision-making.

Youth-Training Data: The Investment No One Measures

If there is one field where the shortage of data causes the greatest damage, it is youth training. A tennis academy decides to hire a coach, choose a method, and build a pathway for a group of students. These decisions usually rest on the personal experience of the person in charge, not on data about which method actually works under that academy's specific conditions.

I once worked with a small academy in southern Vietnam. It had about forty students across various ages. When I proposed collecting basic data — height, weight, training hours per week, results of internal matches — the first reaction was hesitation. No one objected in principle, but no one had time. The coaches taught eight hours a day. An extra thirty minutes of record-keeping daily was a real cost, and that cost produced no immediate result.

After persuading them to try for three months, we discovered something unexpected. The group training the most hours was not the group progressing fastest. The fastest-progressing group was the one training a moderate number of hours but with the highest attendance consistency. In other words, consistency mattered more than total volume. This was a conclusion no one at the academy had believed before, because professional instinct held that more training means better results. Three months of data refuted a belief held for years.

But I must add an important caveat, because I have learned the lesson about the limits of analysis. Forty students is a very small sample. Three months is a short period. The conclusion about consistency may hold in this case but cannot be generalized to every academy. What I want to emphasize is not the specific conclusion, but the value of having data with which to ask questions. Without data, people cannot refute their own biases, because there is nothing to compare against.

The Brand-Substance Test

One of the most important applications of data in my work is comparing media exposure with real foundations. This is a test I apply to both a player's personal brand and a tournament's brand.

For a player, media exposure can be measured by press appearances, social-media engagement, and follower counts. The real foundation is measured by harder things: consistent competitive results across seasons, presence in the training system, the ability to maintain form when conditions change. When these two indicators diverge too much, it is a sign of a brand built more on media than on substance.

For a tournament, media exposure is measured by article volume, view counts, and discussion levels. The real foundation is measured by ticket revenue, sponsorship revenue, the number of sponsors returning the next year, and the quality of the organizing team. One tournament can be heavily covered by the press yet still run at a loss. Another can be little mentioned yet have stable cash flow. Looking only at exposure, one would misjudge both.

I have been wrong in assessing a young player's brand. In 2026, when analyzing engagement data for a group of young players at a club, I focused on engagement growth rate. One individual had growth far exceeding the rest. I concluded this was the most promising brand. But looking back years later, I realized the high growth rate partly came from a very low starting base. A small number multiplied by a large growth rate can still be a small number. I had confused speed with scale. This lesson was recorded as a research cost, not a failure.

The Counterintuitive Angle: The More Beautiful the Analysis, the More Suspicious

This is what I want to say plainly, even if it unsettles some in the industry. The more smoothly an analysis is presented, the fewer gaps it has, the fewer open questions it leaves — the more likely it is that it has filled those gaps with assumptions. Because real data, in practice, is always messy. It always has empty cells, anomalies, and periods that cannot be explained. An honest analysis exposes that mess. An analysis for sale smooths it out.

This is especially true during a major-tournament season. When a big event is underway, the pressure to produce content spikes. Every platform wants analysis, wants predictions, wants numbers. And in that race, speed usually beats accuracy. An analysis written in two hours usually rests on available data, not verified data. The result is a stream of content that looks very professional but has a thin foundation.

I am not saying predictions are meaningless. I make predictions every day, and I believe in their value when they come with clear assumptions and a scope of application. What I object to is the kind of prediction with no assumptions, no scope, only a confident conclusion. A prediction that states no assumptions is a statement of faith dressed in numerical clothing.

There is another paradox I want to raise: in many cases, having more data reduces decision quality, if that data is not read correctly. When a manager has too many tables, they tend to pick the table that confirms what they already wanted to do. This is a well-known bias, but it is especially dangerous in sport, where emotion and performance pressure are always present. Data does not automatically make a decision better. It only makes the decision more justifiable. And a bad decision justified by data is a bad decision that is harder to oppose.

The Limits of Analysis

I have a habit that began after the 2026 World Cup failure: every analysis I write ends with a section on what could make it wrong. Not for self-defense, but so the reader knows the boundaries of the conclusion.

For this article, those limits are as follows. First, the Grand Slam prize-fund figures I cite are published figures for the 2026 season and may change by year, depending on tournament revenue and exchange rates. They serve as a reference for scale and should not be used as exact figures for a specific calculation. Second, the observations about Vietnamese tennis rest on my personal experience as a marketing consultant, meaning they are influenced by a specific viewpoint and do not represent the entire industry. Third, the example of the small academy with forty students is a very small sample, and the consistency conclusion should not be generalized without further verification. Fourth, I do not have access to the internal data of large organizations, so my analysis of them relies on public information.

I state these limits not to weaken the article. I state them because an analysis without a limits section is an analysis trying to appear stronger than it is. And in an industry where I have watched too many confident claims be refuted by reality, cognitive humility is not a weakness. It is an operating tool.

What I want to leave behind is not a conclusion about a specific player or tournament. What I want to leave behind is a question that anyone working in this industry should ask before signing off on a decision: if you strip away the charts and arrows, what is in the source-data column? If the answer is a number of unknown origin, it is better to stop and find the source, even if it slows progress. Because a slow but correct decision can still save a season; a fast but hollow one can wipe out an entire project.

In tennis, people talk about decisive points — break-point, tie-break, match-point. In sports business, the truly decisive point is not on the court. It is the moment a person sits in a meeting room, hand on the report, and decides whether or not to trust the number in front of them. If that person has checked the source, they have the right to trust it. If not, they are playing a wager whose odds have not been calculated. And in an industry already full of uncertainty, adding an unnecessary layer of uncertainty is a choice — a choice we are entirely free to refuse.

Takeaway

If you run a club, an academy, or a tournament in Vietnam, try one thing this week: reopen the most recent analysis you used to make a decision, and trace each number back to its origin. Do not fix anything. Just learn which sources are real, which are estimates, and which do not exist. The result of that test will tell you how safe you actually are. An empty dataset is not frightening if we know it is empty. What is frightening is an empty foundation covered by forty-seven beautiful slides, with no one in the room asking a question.