On a screen in Nagoya, the analysis file opens with nine sections, more than sixty rows, every row colour-coded by risk level. The technical and tactical column is empty. The player data and head-to-head column is empty. The columns for event system, rules and governance, coaching staff, talent pipeline, risk surface, public narrative and industry transmission are empty too. In every cell sits the same sentence: insufficient information, cannot assess.

The file is flawless in form and completely void of content.

What bothers me is that it looks exactly like a real analysis: same layout, same rating scale, same colour palette. A quick reader would take it for the output of a rigorous process. In table tennis right now, frameworks appear faster than data.

I have covered table tennis for the Japanese market across twelve years in the industry, from fact-checking at Sports Illustrated in 2026, through seasons of broadcasting international events, to my current role advising on player development. My job is to read data before I read inspiration. In Japan, the expectations placed on Tomokazu Harimoto have for years run ahead of the actual ranking foundation of the men's team, and that gap is a lesson in how media manufactures pressure.

Nine cells, nine minimum data thresholds

A decent table tennis analysis needs nine cells, each with its own minimum threshold. Technique and tactics need a sample of recent matches, split by opponent type, with serve, receive and rally-length point data. Player data needs the weekly ITTF ranking, points-defence pressure, and head-to-head results divided into three layers: overall, the last two years, and major events. Without those three layers, form is only a snapshot.

The event-system cell requires understanding the WTT points architecture, from Grand Smash, Champions, Star Contender and Contender up to the Finals. A title at Grand Smash level and a title at Contender level are not the same unit of measure, any more than metres and kilograms are.

The competitive-landscape cell needs cross-association comparison: seats inside the world top ten, titles at recent major events, and the depth of the next generation. The rules and governance cell needs four groups kept apart — competition-rule reform, event-system rules, selection rules, disciplinary rulings — because each group has different beneficiaries and losers.

The coaching and pipeline cell needs the age structure of the senior squad, conversion efficiency from junior ranks to the national team, and generational-transition signals. The risk-surface cell needs clear layers: competitive risk, qualification risk, generational-gap risk, governance and public-opinion risk, systemic risk, and opponent risk.

The narrative cell needs market expectation placed next to objective assessment; the gap between those two columns is where a bubble forms. The last cell, industry transmission, needs a chain drawn from equipment and youth development, through events and associations, down to broadcasting, commerce and derivative markets. None of these cells can be filled by feel.

Five steps and the three-season threshold

I run a five-step process that can be reproduced. Step one, collect statistical data. Step two, conduct indirect interviews with coaches. Step three, analyse video of competitive behaviour. Step four, compare against a positional benchmark group. Step five, rank the risks. I standardised this process in 2026, when competitions stopped and I had time to look back at the calls I got wrong.

The pandemic did not stop football; it only filtered out those who judge by intuition.

Step three is the most skipped. Video shows what the scoresheet hides: where a player stands when losing a point, when the legs fade inside a long rally, how they handle trailing in a deciding game. The scoresheet records outcomes. Video records causes.

One threshold is non-negotiable: the observation window. For a young player, a minimum of three seasons, or enough matches spread across different phases of the year. For a form judgement, a minimum of many matches against many opponent types. A short tournament does not create a trend; it creates a snapshot.

A process does not kill discovery. It teaches us to dig in the right place, at the right depth, at the right moment.

Blind spots on both sides

Here I have to argue against myself.

My familiar line — no conclusion without data — has a blind spot. It assumes data always exists and merely needs digging up. In youth table tennis, most tactical data simply does not exist in retrievable form. Nobody records the short-serve and long-serve ratio of a fifteen-year-old at a regional event. Nobody stores footage from a good enough angle.

If I accepted conclusions only when data is complete, I would stay silent about most of what is worth saying. Silence is also a choice, and it is not neutral.

My workaround is to encode intuition as unstructured data: log how often a qualitative judgement repeats across different viewings by different observers, then treat that frequency as a low-weight indicator. It does not replace numbers, but it stops me discarding a signal merely because it has no column in the table.

The second blind spot sits on the opposite side. A complete framework creates a feeling of certainty that the framework itself cannot generate information for. Nine empty cells look like a shield, but they block nothing. The danger peaks when an empty framework is read as a conclusion — nothing unusual here — when in truth it only means nobody checked.

I do not write by feeling. I write what the feet say and what the numbers confirm.

What I propose

I propose a small rule that can be applied immediately: every published analysis must ship with a map of its empty cells. State which cells have enough data, which do not, and why — no source yet, an unreliable source, or a source that exists but lacks an adequate observation window. That label does not weaken a piece. It makes the piece verifiable.

In table tennis, where every round produces thousands of data points and each one can be logged, we are not short of raw material. We are short of the habit of stating plainly what we know and what we do not.

Emotion writes the story, but data keeps the career.

The test sits here: if the next analysis you read were forced to publish its empty cells, would its conclusion still stand?