An Empty Badminton Data Sheet: What an Analyst Does When There Are No Numbers

On Tuesday morning I reopened my scouting file for a badminton tournament in East Java. The spreadsheet had forty-seven cells. All forty-seven were empty. No smash speed, no average rally length, no net-point win rate, no error breakdown at the decisive points. Only the tournament name, the match date, and one line I had written by hand from the stands: “this player starts his footwork later than I expected.”

Four hours of watching, and I came home with exactly one sentence.

Years ago I would have called that a failure. Now I call it data — data about the very gap I was standing inside. That gap shows up more often than people assume, especially in Vietnamese badminton and most lower-tier Asian events.

My job is to turn a match into numbers that can be reused: for coaches, for scouting departments, for newsrooms. I live in Surabaya, work as a data consultant for a football team, and report on badminton for the Indonesian market. But my eye keeps sweeping back toward Vietnam, where I was born, and where badminton occupies a very particular emotional space — not the weekend-crowd sport, but the sport of families waking at five in the morning to drive their children to training.

The BWF World Tour layers itself clearly: Super 1000, Super 750, Super 500, Super 300, Super 100. The All England, Indonesia Open, China Open and Malaysia Open sit at the top. The Vietnam Open sits lower, and the Vietnam International Challenge lower still. That layering is not only about prize money and ranking points. It is about measurement equipment.

At a Super 1000 event I have an instant review system for tight lines, three or four camera angles, a statistics crew behind monitors, and smash speeds published shot by shot. At the Vietnam International Challenge I have one camera operator in a stand corner, a notebook, and a volunteer writing scores in pencil. Nobody is at fault here. These are simply two different data worlds, and readers on the outside rarely know which one they are reading.

Nguyen Tien Minh won bronze at the 2026 World Championships in Guangzhou, Vietnam’s first medal at that level, and competed at four Olympic Games. Nguyen Thuy Linh has held the highest BWF ranking of any Vietnamese women’s singles player in recent years. Le Duc Phat is the next generation, the one expected to extend that reach. Three names, three eras, and one paradox: the more famous a player becomes at home, the less the volume of detailed data about them matches that fame.

Under the BWF’s calculation method, the world ranking takes a player’s best results across roughly ten tournaments within a year. Which means the number on the ranking list is the product of a selection process. It does not say whom the player beat, how they won, or why they lost in a particular round. A ranking number is the tip of an iceberg that even the coaching staff rarely sees in full.

Back to my empty spreadsheet. When there are no numbers, I still have to work. I rebuild the match along three axes. The first is rhythm: rally length, recovery time between rallies, the moment a player begins to accelerate. The second is space: where on the court the player makes contact, and which position they return to after each shot. The third is decision-making: at 18-18, which shot do they choose, and does that choice repeat?

These three axes need no equipment. They need a person who sits long enough, and a notebook honest enough.

In 2026, aged thirty-six, I was a data consultant for Persebaya Surabaya in Liga 2. I used an xG model to advise the coaching staff to push the line higher in the promotion play-off against PSIS Semarang. The model predicted we would generate 1.8 xG. We lost 0-2. PSIS sat deep, conceded the pitch, and every one of our shots was a harmless long-range effort from outside the box. I had ignored PPDA and shot origin. I looked only at total xG and never considered match context. The model wasn’t wrong — I was wrong to let it speak instead of my own eyes.

In 2026, at the World Cup in Russia, I found what the summary tables overlooked. Croatia’s group-stage PPDA was just 9.2 — not the highest-pressing team at the tournament. Yet they led the competition in ball recoveries in the opponent’s half: 12.4 per match. That was not relentless pressing. That was timing. Luka Modric and Ivan Rakitic did not run more than everyone else; they ran at the right moments. Croatia didn’t win the trophy, but they showed me a truth hidden inside a number.

Three years later, at the Euros, I dropped the habit of reading Italy only through PPDA. I measured the average distance between positions and saw that Roberto Mancini’s side controlled matches by compressing horizontal space. Their rate of switching play to the opposite flank was the highest of the tournament, roughly 18.3 times per match. Not running more — stretching in the right place.

Those lessons map directly onto badminton. A singles player can win through smash speed, or win through rally length. Those two paths leave very different traces on court, yet on a scoreboard they look identical: 21-19, 21-18. Facing an empty data sheet, I split errors into two categories. Active errors come when a player chooses a difficult shot and misses — the signature of a system trying to seize control. Passive errors come when a player is forced and has no option left — the signature of a system surviving. The same error rate, two entirely different stories.

In doubles, I count how many times a rally changes direction and how many beats pass from serve to finish. Strong pairs shorten rallies at the third or fourth beat. Weaker pairs extend them, not because they defend well, but because they cannot generate early pressure. A player’s true value lies where they run and when they stop.

But I have to state this clearly, because it is the part most easily skipped.

An empty data sheet is not a blank page for me to write whatever story I like. That emptiness is information. It tells me the tournament is not high enough in tier to carry measurement equipment, the player does not have enough media reach to be tracked, and any conclusion I draw will carry far more error than one drawn from a complete dataset. In other words: my uncertainty is a fact, not a defect to be hidden.

The danger lies elsewhere. The craft of storytelling through unusual metrics carries a subtle temptation: when there are no numbers, the writer begins to invent them. A player wins three straight matches at a low-tier event, and we immediately build a narrative about a new generation. But those three wins may simply mean three weaker opponents, a favourable draw, or a week when the other side’s conditioning dipped. Correlation is not causation. I always ask myself: does this metric actually govern the outcome, or does it merely accompany it?

With Vietnamese badminton, I always present at least two scenarios. Scenario one: the young player is genuinely improving, fitness and technique are better, and the ranking rise is a natural consequence. Scenario two: the ranking rose because a few players in the same bracket were absent through injury or scheduling, and that gain will correct itself within six months. These scenarios are not mutually exclusive, and only continuous tracking data can separate them. Numbers are the prayer, but intuition is the candle — I light both whenever I read a match.

One year taught me this the hardest way. In 2026, when the pandemic stopped every competition, I was thirty-nine and retained through the lockdown. Management asked me to forecast form once football returned. I used the first fifteen rounds of the season and advised the team to keep its possession-based approach. We lost three straight matches when the league restarted, because opponents pressed harder in empty stadiums and we lost the ball inside our own half. My model was missing two variables: the crowd, and the spacing between players. The pandemic taught me that data gets scared too — when the world stops, numbers mean nothing.

So when I watch a Vietnamese player at a Super 100 event on single-angle footage, I do not conclude. I record what I can observe, flag what I cannot, and leave the conclusion open. Readers deserve to know where I know, where I infer, and where I know nothing at all.

Over the coming tournament cycle I will track four signals. First, how deep Vietnamese players progress at Super 300 level and above, because that is where full measurement data exists. Second, schedule structure — a player entering four events in six weeks may be seeing a ranking rise that is really an advance, with the bill arriving later. Third, whether domestic training centres begin keeping records: if they move from paper notebooks to spreadsheets, that is a bigger signal than any medal. Fourth, the arrival of analytics partners at domestic tournaments, because that is when the data gap starts to close.

That evening I left the spreadsheet empty and saved it anyway. At the top I added one new row header: “what I have not yet measured.” In all likelihood that row will stay empty longer than every other row combined.

The question I want to carry into the next tournament is not which player will win. It is this: having grown used to reading a sport through incomplete numbers, do we have the courage to say we are reading a fragment, rather than the whole story?