The Blank Fields of Volleyball Data: From Japan's SV League to the Tran Thi Thanh Thuy Story

The Blank Fields of Volleyball Data: From Japan's SV League to the Tran Thi Thanh Thuy Story

2:40 a.m. in Nagoya

On Tuesday night I opened a scouting report sent by a contact in Tokyo. The file had two team names, a match date, a rotation diagram, and a column labelled "information points." The column was empty. Not a line, not a word, not a figure. Just white boxes arranged neatly like rows of seats no one had sat in.

Four hours earlier I had walked out of a gymnasium in Aichi. Everything there was painfully clear: the slap of the ball on the wooden floor, shoes peeling off the surface, an assistant coach calling out the number five position in a clipped syllable. I saw a setter take one step too many toward the net. I saw a middle blocker half a beat late on a first-ball read. I saw an attacker look down at the floor before the ball had even arrived. When the match ended, most of what I had just watched did not exist in any official data file. And tonight that data file had nothing to record at all.

This is a story about blank space — about how volleyball, arguably the team sport with the densest event frequency, is building its information systems on a foundation thinner than most people assume.

Context: a sport growing faster than its data infrastructure

Over roughly a decade, Japanese volleyball has risen at a speed that surprised even the people working inside it. Japan's men's national team climbed to No. 2 in the FIVB world ranking in mid-2026, the highest position Japanese men's volleyball had reached in decades. At Paris 2026, Japan led Italy 2-0 in the quarterfinal and lost 3-2 — one of the most replayed matches of that Olympic tournament. Japan's women, known as Hinotori Nippon, went out in the group stage, while the men, Ryujin Nippon, became a media phenomenon far beyond the sport.

In October 2026, Japan's domestic championship was restructured into the SV League, replacing the V.LEAGUE that had existed for nearly three decades. The reform carried an explicit professionalisation agenda: arena standards, contract standards, broadcast standards and, most relevant here, data standards.

Across the sea, Vietnamese volleyball has moved too. Vietnam's women's national team has become a consistent presence in the upper tiers of Asian and world competition, and one name has come to symbolise that shift: Tran Thi Thanh Thuy. She was among the first Vietnamese volleyball players to move abroad professionally, playing for PFU Blue Cats in Japan before joining Kuzeyboru in Turkey. For a Vietnamese reporter living in Japan, she is the rare intersection of the two volleyball cultures I follow daily.

But when I tried to reconstruct Thanh Thuy's overseas career through data, I ran straight into the same blank space that Tuesday-night file represented. No unified database lets me compare her attack efficiency in Japan with her output in Turkey, then set both beside her numbers at SEA Games or Asian championships. Three recording systems, three definitions of a metric, three levels of transparency. I have the player's name. I have memory. I do not have a continuous series.

That is the paradox of modern volleyball. The sport generates an enormous volume of raw data every season, and most of it dissipates the moment a match ends — unstandardised, unarchived, never stitched into a long-term narrative.

Volleyball is a sport of extremely short causal chains

To understand why volleyball data is hard, you have to understand the structure of the sport itself.

A volleyball match is a sequence of rallies that are very short — usually a few seconds. Each rally is decided by a chain of almost instantaneous links: the serve, the receiver's position, the quality of the set, the setter's tempo choice, the opponent's block positioning, the attacker's approach angle, the landing point. The whole chain unfolds in a window in which a casual viewer sees only one hard swing.

The consequence is that every volleyball metric is a chain-level metric, not an event-level one. And every chain-level metric depends on a chain of human judgement.

The clearest example is perfect-pass rate. It measures the share of first contacts delivered to the ideal position, allowing the setter to run the full tactical menu — high-tempo wings, back-row attacks, combinations with the middle. But "perfect" is a judgement. Two scorers watching the same contact can reach two different conclusions, and both can be right. One grades by where the ball landed. The other grades by whether the setter still had the option of the number-three run. Same rally, two numbers.

That is why volleyball data has uneven reliability from the moment of collection, before any analysis happens at all. When a metric passes through three layers — the scorer, the software, the analyst — error is not eliminated. It is amplified.

Dedicated platforms such as DataVolley and VolleyStation have standardised a great deal: rally coding, attack-type classification, court zoning, block positioning. They have not standardised the entire volleyball world. An SV League match in Japan is coded at very high resolution. A lower-tier Asian league match may have nothing but a basic scoresheet posted by the organiser. Placing those two files side by side and averaging them is an anti-scientific act, yet it happens daily, usually because the analyst has no other option.

Standard metrics and systematic blind spots

The metric set familiar to anyone working in professional volleyball is small: perfect-pass rate, side-out rate, break-point rate, attack efficiency by attacker, blocks per set, ace-to-error ratio, dig success rate.

This set is strong at detecting repeating patterns. It has at least four enormous blind spots.

The first is decision quality out of system — attacks that follow a broken first contact. A significant share of modern rallies fall into this category, and in those rallies individual attack efficiency is unfairly punished. An attacker receives a set nearly two metres off the net, faces a loaded double block, and hits out. The scoresheet records an attack error. Anyone who was in the gym knows that rally died at the first contact.

The second is the value of actions that produce no point. A read block that touches the ball and changes its trajectory for a teammate to dig often simply does not exist in low-tier records. A pressure serve that forces an opponent into a bad position, producing a weak attack and an easy defensive play, is typically recorded as a blank cell.

The third is substitution rhythm. Volleyball caps substitutions, and rotation management creates very different structures across a match. A team can rotate to hide a stretch where two attackers are both in the back row. Those choices directly affect outcomes and almost never appear in any visible metric.

The fourth, and for me the most important, is continuity over time. Volleyball is a sport of short seasons, interrupted competitions and players who change countries constantly. An attacker may play in Japan from October to April, in Turkey from October to May the following year, then return to the national team in summer. Three competitive contexts, three data standards, three entirely different physical-assessment systems. Tracking one player across a career effectively requires rebuilding the data from scratch.

I once told a colleague in Tokyo that covering volleyball sometimes feels closer to archaeology than journalism. You find sherds at three different sites and have to reconstruct a vase no one has ever seen whole.

The scorer: the human link in the data chain

In every volleyball statistics system there is a role few fans know about: the technical scorer. They sit in a corner of the arena, left hand on a coding keyboard, eyes on the ball, making roughly three to five classification decisions per rally.

In a five-set match, that can run into thousands of decisions. None of them is systematically cross-checked. At many tournaments, one scorer covers an entire match.

This does not make volleyball data useless. It means volleyball data has a property I carry in my notebook in one sentence: data does not lie, but the person choosing the data knows exactly how to lie. The scorer is not lying on purpose. The analyst selecting metrics is not lying on purpose. But every time a rally is placed into a coding cell, part of the truth is permanently discarded, and nobody records what was discarded.

The only way to control this is to record the metadata too: who collected it, under which standard, on which software version, whether it was rechecked, and what percentage of rallies a second observer confirmed. Almost no league publishes this. The result is that when two sources conflict, we have no way of knowing which is right — only which is cited more.

I have spent many evenings in arenas counting for myself. Not to prove anyone wrong, but to understand the margin of error. The result made me calmer about every number I read afterwards.

Japan: from V.LEAGUE to SV League, and the standardisation problem

The V.LEAGUE-to-SV League restructuring in October 2026 carried an explicit data ambition. Organisers set targets for broadcast standards, presentation standards and statistical standards so the league could reach international audiences.

But standardising volleyball data runs into a structural barrier: clubs do not share internal data. Detailed scouting reports, load monitoring, injury data, opponent analysis — all are competitive assets. A league that wants to publish deep data must convince clubs that publishing will not weaken them. That negotiation is long, and virtually everywhere in the world it is unfinished.

In Japan there is an added cultural layer. Japanese sports media has a tradition of respecting the internal space of a team. A reporter seeking tactical data usually goes through relationships built over years, not an official release channel. I am a foreigner living here, and I learned that patience in professional relationships in Japan is not a tactic — it is the precondition for being heard.

Vietnam: cross-border blank space and what it costs

Tran Thi Thanh Thuy is the ideal case for seeing the whole problem, because her career runs through the most disparate data systems available.

At SEA Games and Asian championships, data on her is scattered across organiser scoresheets, regional federation releases and domestic media compilations. Low resolution, but relatively continuous, because it is the same competition system.

In Japan, at PFU Blue Cats, the standard jumps — far more detail, court zoning, attack classification, round-by-round efficiency. But that standard does not connect to the previous data, because definitions, coding and display language all differ.

In Turkey, at Kuzeyboru, the story repeats in a new variant. The Turkish league has one of the largest media and data audiences in Europe, yet deep technical data still sits with the clubs.

The result is that a reporter wanting a serious four-year assessment has to stitch together at least five sources with five definitions of "attack efficiency." Anyone who does this knows the join is imperfect. But refusing to join it produces no story at all — and having no story is also a form of lying. Just a quiet one.

What I saw sitting alone in an empty arena

In 2026, when the pandemic shut down almost the entire competition calendar, I spent time in arenas with no spectators. It shaped how I see this job.

An empty volleyball arena does not go quiet in a comfortable way. Sound is still there but changes character. The ball against a hand rings loudly enough that you hear the force in each finger. Footsteps on the wooden floor become a rhythm you can count. A coach speaking across the court is audible word for word.

That is where I understood something I still carry: listening to an empty arena taught me that noise was never the audience. Real presence and manufactured noise are not the same thing. A packed stand full of shouting may contain no one actually watching. An empty arena may be full of people following on a screen.

I think about that whenever I read a data table packed with numbers. A dense table can hold very little real information. An empty table — like that Tuesday-night file — can hold something important: that something upstream has broken, and that you should stop before you write further.

The contrarian angle: we do not lack data, we lack provenance

The standard industry response to any analytical problem is to demand more data. More sensors, more cameras, more metrics, more dashboards. I think that response is right on the surface and wrong at its core.

Modern volleyball does not lack data. A professional match generates thousands of data points. The problem is that this data lacks provenance: no identifier for who collected it, no standard version, no timestamp, no cross-check record, no revision history. A number without provenance is not data. It is a claim with no one accountable for it.

The practical consequence is concrete. When three statistical sources publish three different numbers for the same player in the same match, readers cannot adjudicate, so they pick the number that matches what they already believed. At that point data stops describing reality and starts confirming bias.

My view is that the correct priority for volleyball over the next few years is fewer metrics and better metadata. A league publishing three metrics with full provenance, standard version and cross-check rate is worth more than a league publishing thirty unverified ones.

In Japan this direction has promise, because the working culture here respects process and archiving. In Vietnam it has promise because the scale is still small — which means building a standard from the start is far cheaper than repairing an entrenched system.

Data is a character, not a judge

There is a habit I deliberately keep. Whenever I read a statistics table, I picture the person who typed it. Were they tired or alert. Were they distracted by a rally on the next court. Did they have time to review the controversial contact.

This turns every series of numbers into a character with a temperament, blind spots, and a capacity for very sophisticated lying that the liar may not even recognise. A high attack percentage can be the mark of an outstanding attacker. It can also be the mark of an attacker whose setter feeds her the favourable situations while teammates absorb the bad ones. Same number, two opposite stories.

That is why I never use data to end an argument. I use it to open one.

Final thought

That Tuesday-night file is still on my machine. I have not deleted it. I keep it as a reminder that most of the work of a sportswriter lies not in answering, but in recognising when there is not yet enough ground to answer.

In volleyball, a rally lasts a few seconds, and inside those seconds are hundreds of small decisions no scoresheet records. My job is to go looking for those decisions — in the arena, in the corridor, in a blank column of a report that arrived at almost three in the morning.

If Asian volleyball builds a data standard in the coming years that has provenance, verification and cross-border traceability, we will understand more than the careers of players like Tran Thi Thanh Thuy. We will understand ourselves — the people who chose to see this sport through numbers, and who need to learn to see it with the right amount of doubt.