Billiards Is Not One Sport: The First Step of Any Analysis Is Identifying the Discipline

Two in the Morning and an Empty Spreadsheet

Two in the morning, Hai Phong. Outside the window there was nothing left but the sound of trucks crossing the bridge. I opened the archive folder for a billiards event that had just ended, named according to the convention I have kept for four years in this job: event code, date, discipline. Inside was the spreadsheet I build for every event — nine sheets, each one a dimension of analysis, from player identity all the way to prize structure.

I clicked the first sheet. Empty. The second. Empty. I scrolled to the very bottom and found a single header row sitting there bare, without one populated cell.

In four years I have seen plenty of thin articles. A results brief with only a scoreline. A tournament notice with only a date and a venue. Those are still analyzable, just barely, and every conclusion has to carry a warning line about reliability. What sat in front of me that night was different in kind: a null input. The gap between a data-poor article and a null input is far wider than it looks, and it took me two more hours to name correctly what I was holding.

The first principle I set for myself at seventeen, after a football match I read completely wrong, is this: before believing any metric, I have to answer who measured it, how they measured it, under what conditions, and what it is hiding. That night, at the very first question, I was stopped. Nobody measured anything. There was nothing to measure.

Data never lies, but I have misheard it before. This time was different: there was no data making any sound to mishear.

Why I Built a Nine-Dimension Checklist

In Vietnamese, "billiards" is an umbrella name for at least five different games, separated by rules, by table, by balls, and most importantly by how a cueist is measured.

Snooker is played on a large table with tight pockets and fifteen reds, and the scoreboard is built from century-break sequences. Pool — 8-ball, 9-ball, 10-ball — uses a smaller table, plastic balls, and measures rack win rate along with break quality. Chinese 8-ball places 8-ball rules on a snooker-style bed with pinched pockets, producing a hybrid with its own data grammar, where safety play and attacking play are weighed on a different scale than in pool. Three-cushion carom has no pockets at all, and its unit of measurement is points per inning. Russian pyramid uses noticeably larger balls and even tighter pockets, to the point where the success rate of a shot that other disciplines would call routine is fundamentally different.

Placed side by side, these five games share no common metric system. Century breaks mean nothing in carom. Rack win rate means nothing in snooker. Points per inning mean nothing in pool. Identifying the discipline is therefore not an administrative labeling step — it is a gate. Fail the gate and every calculation behind it is meaningless, even when the arithmetic is correct.

I built my nine-sheet spreadsheet on exactly that logic. Sheet one: discipline identity. Sheet two: player data and form. Sheet three: tournament system and format. Sheet four: the power map by nation and generation. Sheet five: rules, governance and compliance. Sheet six: career ecosystem and psychology. Sheet seven: the risk matrix. Sheet eight: public narrative and expectations. Sheet nine: the industry transmission chain.

The reason it has to be nine sheets is that I have paid for skipping one. In 2026, analyzing the no-spectator stretch, I learned that sample size and significance have to be published alongside the finding, otherwise a correct number still gets misread. Before that, in 2026, I once leaned on a single metric to reach a conclusion and reality contradicted me the same evening. From those two episodes I developed a habit: every analysis opens with a list of conditions to verify, and only when that list is answered do I allow myself to write a concluding sentence.

That night, not one line of my list was answered.

Nine Conditions to Verify

One. The discipline is a gate, not a label

The input in my hands had no tournament name, no rules terminology, no player name. Not one fragment of those three existed.

The distance between snooker and Chinese 8-ball is not about ball color. It is about which question gets asked. In snooker, the central question is the ability to build a break within a single visit and to control the cue ball over long distances — things that only matter on a long table with tight pockets. In 9-ball, the central question is break quality and rack win rate, because the shape of the match is decided from the opening shot. In carom, the central question is cushion accuracy and the ability to accumulate points across many innings. All three are called billiards, and their question sets do not intersect anywhere.

The consequence is that a good metric in one discipline can be a meaningless — or actively misleading — metric in another. If I take a pool player's rack win rate and use it to evaluate a snooker player, I have not made a small unit error. I am comparing two fundamentally different things and calling them by one name.

With the discipline unidentifiable, this is a mandatory stop. I can confirm this with high confidence, because a null input is verifiable — I count populated cells and get zero.

Two. Player data and form

The second sheet asks familiar questions. For snooker: ranking-event titles, century breaks, 147 maximums, head-to-head records, and performance in long-format events.

For 9-ball: rack win rate, break quality, decider-rack win rate. For carom: average points per inning and consistency across rounds.

For Chinese 8-ball, the metric set is an awkward blend of both worlds: win rate still matters, but so does shot quality on a pinched bed, where a small error in pace can turn an attacking shot into a gift that puts the opponent at the table.

There is one test I always run here, the data-versus-fame test. A cueist can be famous for beautiful moments clipped into short videos while their long-run competitive record tells a different story. That test only runs with at least one quantitative anchor. That night, my anchor count was zero.

Three. Tournament system and format

A billiards event is not a monolith. Snooker has the group known as the Triple Crown — the World Championship, the UK Championship and the Masters — sitting at the top tier. Below that sit ranking events, then invitationals, then commercial events, then seniors circuits. Each tier pays differently and demands different preparation.

Format is what decides the room for upsets. A match stretched over nineteen frames and a match over seven frames do not share a probability distribution. The fewer the frames, the louder a single lucky shot echoes and the more the skill gap is compressed. This is why I never offer a judgment on a tournament's upset potential before knowing the frame count for each round.

Prize structure is also a strategic variable. If the money is stacked too heavily toward the champion, players rearrange their calendars — they concentrate on fewer events with more weight. Without a tournament name I cannot tier it, cannot estimate upset risk, and cannot read scheduling strategy.

Four. The power map and squad depth

The world billiards power map pivots on a few familiar axes. The United Kingdom is the cradle of snooker and still holds the densest tournament ecosystem. China has been the fastest-growing market for years and is a major recruitment source. Other nations contribute by discipline: continental Europe is strong in carom, Eastern Europe has its own tradition in Russian pyramid, Southeast Asia has a certain pool foundation.

Drawing this map requires cohort data, not individual data. You need to know the depth of the development pipeline: how many junior cueists each nation trains properly, and how many of them reach main-draw stages. You need generational data to talk about a handover — the 2026 cohort dominated snooker for years, and the question of who replaces them can only be answered with titles split by age group.

There is one cross-discipline hypothesis I have tracked for a long time: rising prize money in Chinese 8-ball may pull some snooker players across. That is a hypothesis requiring multi-season migration data, not a conclusion to publish for the sake of it. Without data, I leave it labeled as a hypothesis.

Five. Rules, governance and compliance

The governance structure is fairly clear. Professional snooker is run by the professional players' association and by the body that operates the tour. International pool sits under its own association system. Billiards in each country has a national federation and is shaped by both international structures and domestic sports policy.

The single most important theme in this industry for years has been betting and match-fixing. It matters because the sport has technical features that favor manipulation: a missed shot looks identical to a genuinely missed shot, and spectators have no way to tell the difference from a single viewing.

Because of that, I keep a strict rule on this topic: raise it only when there is a real signal — an investigation notice, a disciplinary ruling, a regulator's decision. Attaching the match-fixing theme to a named individual with no signal at all is to cast baseless suspicion on someone's livelihood. I do not do it, even when it draws more readers.

Six. Career ecosystem and psychology

Sheet six asks questions pure data cannot answer. What does a cueist live on? Prize money is extraordinarily skewed: a small group at the top lives entirely off competition, while the rest teach, coach, play commercial events, or open a hall. Is the playing rhythm reasonable, and who manages that rhythm?

On psychology, I look for metrics few people check. Decider-frame win rate. Final-stage performance, isolated from overall performance. Misses on key balls in front of large crowds. Off-table pressure — family, contracts, media — does not appear on the scoreboard but appears in results.

In billiards, the psychological unit is much smaller than in football. There is no eleven-man collapse. There is one person, one cue, one silence before the stroke. But the sequence is long: a deciding frame can arrive after four hours of play, and what gets measured is not the emotion at that instant but the entire process leading to it.

Seven. The risk matrix

My matrix has six families: competitive, career and income, compliance and reputation, rules, psychological, and systemic.

Assigning risk levels to these six families without a subject and without data produces what I call false precision. A fully populated risk matrix looks very professional. It is also very easy to misread, because the reader has no way of knowing that every cell was derived from a null input.

The real risk here is not in those six families. It sits at the process level: an extraction step ran and produced no output, and had I not checked, I could have kept writing as if everything were normal.

Eight. Public narrative and the expectation gap

Every cueist and every tournament carries a story being told. That story has a cycle: it heats up after a result, holds if more results support it, cools after a defeat. My job is to place the story on a timeline and compare it against the underlying data.

There is one check I call the prodigy filter. A big title won young generates noise, but it only means something long-term when placed inside a continuous run of results across multiple seasons. One flash and a development trajectory are two different things, and only sequence data separates them.

The gap between market expectation and objective assessment is the zone I care about most, because that is where value sits. But to measure that gap I need both sides. That night, I had neither.

Nine. The billiards industry transmission chain

The industry runs on three tiers. Upstream is infrastructure: halls, tables, balls, cues, chalk, and training facilities. Midstream is players, tournament systems, and broadcast — where value is created and where every major event starts. Downstream is sponsorship, commerce, derivatives, and collectibles.

The transmission mechanisms are known: a champion raises hall traffic in his home country over the following months; rising prize money in one discipline shifts the talent flow toward it; a governance scandal slows the sponsorship cycle for one to two seasons.

But every transmission chain needs a specific originating event. A title. A scandal. A policy change. Without an originating event, I am just describing a stationary machine and calling it analysis.

The Biggest Risk Is Not Missing Data

There is one thought I kept returning to after that night, and it runs against intuition.

The problem is not a lack of data. Missing data is normal and manageable: state the limits, lower the confidence level, wait for more sample. The real problem is a process that keeps running when the data is empty. In sports analysis, the rewards go to the person who makes a prediction. They do not go to the person who declines to make one. This profession rewards confidence, not well-timed silence.

So the greatest pressure I feel in front of an empty spreadsheet is not the sense that I do not understand enough. It is the sense that I could write a complete piece, one that reads fluently, with numbers and tables, almost entirely constructed out of thin air. That happens far more easily than readers assume. It is why I built the nine-sheet spreadsheet instead of writing directly: a frame forces me to see the empty cell.

In the other direction, I do not want to push so far that caution becomes a religion. The human element is not an excuse to ignore data, and "feel" is not a verifiable data source. Data analysts entering the locker room is a real trend carrying a real risk: modeling a cueist as a string of results while ignoring stroke tempo, the feel of the cloth, and the silence before a deciding shot.

But in billiards the psychological unit is much smaller than in team sports, which also makes it easier to undervalue. A single missed shot is an error. Three misses of the same type are a signal. Telling those two apart is the whole job, and both require data before any conclusion.

What I Will Track Next

I will not publish an analysis for that tournament. The spreadsheet is closed; I am re-running the extraction step and putting three tracking fields at the top of the file: source provenance, discipline identity, player identity. When those three fields are populated, the nine sheets reopen and I resume exactly where I stopped.

I do not write to convince anyone. I write so that the data has a witness. And on one night in Hai Phong, what I had to witness was not a wrong judgment but a broken process at the very first step. An empty spreadsheet is still data. It is just data about me, not about the match.