The Empty Cell in the Spreadsheet and the Discipline of a Volleyball Analyst

Late June, I sat in front of a screen with a spreadsheet already open. Twelve columns, four hundred rows, and everything blank. The block-efficiency column held not a single figure. The ace-rate column was pure white. Even the average rally-duration column, something I have logged by hand across twenty-two years of live commentary, had nothing to fill. The spreadsheet sat there, clean to the point of meaninglessness, and I realized I was facing the material analysts fear most: material that does not exist.

A newcomer fills empty cells with guesswork. He writes "by observation, Team A's block looks slower," then turns that sentence into a claim, then turns the claim into a forecast. I once did exactly that. In 2026, I confidently staked two hundred million dong on Germany reaching the World Cup quarterfinals, based on a model showing 68% average possession and a 91% pass-completion rate. Germany lost 0-2 to South Korea and went out in the group stage. That night I rewatched the footage and found they had covered 4.2 km less per man than in qualifying. My model had no column to hold that psychological signal.

Vietnam's volleyball transfer window this year is another version of the same problem. Every day I receive dozens of messages: this club is about to sign that outside hitter, a team is preparing to change coaches, a national-team player is weighing a move abroad. All of it is information, and almost none of it has a verifiable source. Transfer noise drowns the signal. The issue is not the volume of news but that the volume of news does not come with a matching volume of evidence.

In volleyball we have a fairly clear system of metrics. An outside hitter's scoring efficiency. A middle blocker's block-success rate. A libero's perfect-pass rate. Ace rate against service errors. These figures are measurable, comparable across rounds, and, most importantly, sourced. When a team signs an outside hitter, I do not read rumors about her salary. I open three seasons of statistics, cross-check scoring efficiency against attack attempts, and only then look at secondary factors: age, injury history, adaptability to a new tactical system.

But the transfer window poses a harder question. Most of the data fans access has already passed through a layer of processing. News reports, posts, comments. No one publishes the structure of a release clause. No one publishes a club's true wage bill. No one publishes medical results. What we have is the surface, and the surface is always cleaner than the truth. That is why I open every analysis with a raw data table, not with a story.

In the 2026-18 season, after fifteen Premier League rounds, I noticed Burnley had a total xG of just 15.2 but had actually scored 18 goals, an overperformance of 18.4%. The bookmakers still rated them underdogs with relegation odds of 5/1. I staked five hundred million dong on Burnley finishing top ten at odds of 3.25. They finished seventh on 54 points, above Arsenal, and I collected 1.6 billion dong. What I learned was not that xG predicted correctly, but that the market had mispriced a team because it only read what was lit by the spotlight. Burnley never played pretty, but always played right.

I tell that story not to praise myself. I tell it because it taught me a principle I apply to volleyball too: I do not look for value where people shine a light, but where they forgot to plug in the power. In volleyball, the place people forgot to plug in is the block, not the number of successful blocks but the delay in the hands' movement before the ball is set. It is the breathing rhythm of an outside hitter after three long rallies, when she starts attacking with the wrist instead of the whole body. It is the setter's second-ball decision in a chase, when the safe choice and the risky choice sit a thousandth of a second apart.

These things do not appear in the official statistics. They have no column. And that is the crux of the empty-spreadsheet story at the top of this piece. When a column does not exist, you cannot conclude that the value is zero. You can only conclude that you have not measured it. The difference between "no data" and "data equal to zero" is the difference between an analyst and a fabricator.

When a Vietnamese volleyball team signs a foreign player, the report will cite her average points in her old league. I always ask the reverse: those points were scored against which block? An outside hitter averaging 25 points a match in a league with weak blocking is not the same player as one averaging 25 points in a league with strong blocking. Same metric, different meaning. For that reason, I require myself to verify every claim with at least three different indicators, always adjusting for opponent strength.

In volleyball, the three indicators I use most are scoring efficiency per attack attempt, block-success rate per block involvement, and perfect-pass rate. These three measure different facets of the same player. When all three point the same way, I trust it. When they conflict, I stop and find out why. A player with high scoring efficiency but a low perfect-pass rate is usually someone riding luck inside a strong cover system, not someone creating value herself.

I rank sources in three tiers. Tier one is official data from a tournament organizer or federation, something verifiable and accountable. Tier two is journalism with an accountable outlet and a reporter on the ground. Tier three is social media, where a hastily taken photo can become evidence after a single share. Most transfer noise comes from tier three, and most real informational value sits in tier one. My job is to pull every claim back to tier one, or, if I cannot, to label it clearly as tier three.

After the Germany 2026 lesson, my writing gained a section I call non-sporting context. It is where I cross-check performance data against things statistics cannot measure: travel schedules, locker-room state, fluctuations in distance covered. A volleyball team playing three matches in four days across three cities will block differently than the same team on full rest. The performance metric is not wrong. It simply does not tell you why it is low.

This is where I differ from most data analysts. They believe that enough data will tell the story by itself. I believe data can only tell the story its collector allows it to tell. Clean data does not mean a clean reality. Germany 2026 taught me that costliest lesson, and I paid for it with two hundred million dong and the ego of a man who had just won big on Burnley.

In 2026, when stadiums worldwide closed for the pandemic, I realized I stood before a rare opportunity. Empty stands were like a giant laboratory, and I was the one standing inside observing. With no crowd noise, external psychological pressure was almost entirely removed. What remained was pure tactics and team spirit. Teams dependent on crowd fervor began to decline. Teams with good structure held firm.

I applied that logic to domestic volleyball. When a team loses an outside hitter to injury, people usually say the team has weakened. I do not measure by feeling. I compare the team's performance with and without that player, then check whether the block and the defensive system changed accordingly. Often the team has not weakened. The system merely reallocates roles. A middle blocker is pushed to carry more, a libero is handed more balls, and the overall result barely changes. An individual star is not the structure. The structure is what decides.

Based on my experience watching matches in the national championship and at the National Games, I see a repeating pattern. A team that wins through one outstanding individual tends to collapse the very next season, when that individual is figured out or transfers away. A team that wins through a stable system tends to hold its position for three to four seasons. The standings do not say this. You have to read the structure to see it.

That is also why I distrust the transfer arms race among big clubs. The transfer market buys stories; I only buy evidence. An expensive signing produces a headline, not a system. Real value usually sits with small clubs, where they must pick players by data because they have no money to buy glamour. That is where I find the best investments, in volleyball as in football.

In this transfer window, I track three structural signals. The first is contract-clause structure, because release clauses and durations reveal a club's true ambition. The second is the wage bill, because it shows whether a team is building around a star or around a system. The third is agent activity, where real information and inflated information meet. These three signals receive far less coverage than headlines about a foreign player's average points.

Back to the empty spreadsheet at the top. After checking again, I understood what had happened. My data source had failed at the extraction stage, not the match itself lacking figures. This is a different kind of error from a team genuinely playing poorly. It is a system fault of my own, upstream, not the truth of the match. Had I filled the empty cells with guesswork, I would have written a false analysis of a match I never actually measured. The discipline of an analyst lies not in always having an answer, but in knowing when to say there is not yet enough data to assess.

I know this sounds unattractive. Audiences want a firm conclusion, a forecast they can bet on. But a forecast without a basis is not a forecast. It is a lie presented in the form of data. And in twenty-nine years of watching this industry, I have learned that lies dressed as data are the most dangerous kind, because they persuade readers with an appearance of precision.

There is another temptation I must actively avoid: absolute faith in data I have collected myself. Because I have labored over the numbers, I easily treat my dataset as truth. But every measurement has limits. Scoring efficiency does not measure psychological pressure. Block-success rate does not measure the quality of the set that preceded it. A beautiful metric can be the product of systematic bias. So in each analysis I devote a section to stating the limits of the measurement I am using, rather than presenting it as absolute truth.

I also avoid tearing down any star. When a team fails, the reflex of the crowd is to assign blame to the most prominent individual. I turn the focus back to structure and coaching decisions. A collapsing block is not because one middle blocker stood in the wrong place, but because the blocking system was not adjusted when the opponent changed their attacking rhythm. Blaming an individual is the fastest way to avoid analyzing structure.

Vietnamese volleyball is at an interesting stage. Tournaments are becoming more professional, data is growing, and fans are increasingly comfortable reading statistics. But more data does not automatically mean more understanding. If people use data to confirm what they already believe, then data merely makes prejudice seem credible in an artificial way. The value of data lies in its power to force you to change your mind.

In volleyball, that means accepting that a team can win while losing every metric, and lose while winning every metric. I have witnessed matches where a team fully controlled the game yet collapsed at the decisive rally because of one wrong set decision. The metrics said that team deserved to win. Reality said it lost. Both are true, at two different levels.

Names like Tran Thi Thanh Thuy or Nguyen Thi Bich Tuyen appear in almost every transfer conversation. That does not mean they will move clubs. It only means their names sell news. I do not buy news. I buy evidence.

After the Germany 2026 lesson, I stopped asking what the data says and started asking what the data is hiding. The empty spreadsheet that late-June night taught me another version of the same lesson: sometimes the data hides nothing, it simply never existed. And in that case, the most honest thing an analyst can do is close the laptop, fix the fault upstream, and come back when there is something real to measure.

At 45, I know the market is always wrong, but wrong in ways that can be anticipated, as long as I am willing to admit when I do not yet have enough data to say anything at all. The next round will open, the spreadsheets will fill again, and transfer noise will sweep everyone up. My job stays the same: to look where people forgot to plug in the power, and when that place is too dark to hold anything, to say honestly that I have not yet seen a thing.