The Data Void in British Billiards: When a Perfect Shot Has No Measure

It was a Tuesday night at the Crucible Theatre in Sheffield, and the quarter-final ran nearly five hours. The winner advanced to the semi-final at 13-11, and the big screen showed only three lines: frames won, breaks over 50, and a pot success rate rounded to the nearest whole number. That was everything the audience was given. No metric indicated who controlled the table, who played the better positional shot, who won the decisive safety exchanges in the twenty-second frame. I stayed behind afterwards, rewound the recording, and realised I was trying to analyse something this sport has never measured.

I follow professional British billiards as a data journalist, but my starting point lies in football. In 2026, barely eighteen and a first-year Economics student in London, I launched a World Cup data blog. The first match I picked was Germany losing 0-2 to South Korea: the reigning champions generated 2.1 xG, held 74% possession, and scored nothing. I showed that their shots all came from wide positions, averaging just 0.08 xG per attempt. My econometrics lecturer read it and remarked: "Data does not lie, but it is speaking a language you do not yet fully understand."

From that I learned a principle: results are noise, process is signal. When I moved to covering billiards for the British market, I carried that principle with me. I spent my first two seasons simply observing, not writing a single line of analysis, because I believe you must understand a sport's structure before you measure it. The deeper I dug, the clearer it became that professional billiards sits exactly where football stood before the xG revolution — a point where every judgement about shot quality still depends on the human eye.

To explain this gap, you have to separate two layers of data. The first is outcome data: frames won, century breaks, titles. This layer is thick and reliable. According to public World Snooker Tour records, the World Championship has been staged at the Crucible Theatre since 2026, and feats such as Ronnie O'Sullivan's seven world titles or Stephen Hendry's seven are historical benchmarks anyone can verify in seconds.

The second layer is process data: pot quality, positional quality, safety decision quality. This layer is almost entirely empty. Football has xG, PPDA, progressive passes. Billiards has only pot success and safety success — binary metrics, right or wrong, that cannot distinguish a hard pot from an awkward angle from an easy pot from a favourable position. A player potting nine in ten may simply be choosing the easy shots; a player potting six in ten may be the one taking the decisive ones. The statistics table does not tell me who is who.

The problem became clearer when I tried what I call "hypothesis testing": pose a question, gather data across several seasons, and only then conclude. In the summer of 2026, when football froze during the pandemic, I rewatched twelve Liverpool matches before the season was suspended and found their average PPDA was 9.8 — opponents completed fewer than ten passes before losing the ball. Empty stadiums, the manager's voice clearer than ever, and the data too. I wanted to do the same for billiards: isolate the crowd variable, measure shot quality in silence. But I had no data to measure. A football analyst short on data can buy it from a provider; a billiards analyst short on data is forced to invent it, and I refuse to do that.

This is what makes the story notable. Billiards is a sport of pure geometry, yet it has among the weakest measurement infrastructure of any professional sport. A single shot can be described by its approach angle, force, contact point, and the cue ball's resulting position. All are quantities cameras and sensors could capture. But nobody measures them, because there is no market for it.

What is telling is that other sports moved ahead. Tennis has had Hawk-Eye tracking every ball since 2026, and has since built metrics such as second-serve points won and net-points won. Darts has systems measuring throw force and dart placement. Even pool has trial events with table sensors. English billiards, with the longest tradition of all, sits outside that measurement race.

Here a paradox appears that I want to pause on. Football needs xG because its flow is chaotic: hundreds of passes, dozens of shots, and nobody remembers which was truly dangerous. Billiards is the opposite — every shot is a discrete, deliberate, visible act. Viewers see exactly what the player did. Could it be that because the process is already visible on the table, billiards does not need a proxy metric like xG?

I will not rush that argument, because there are at least two other explanations for this data gap. The first is economic: billiards' audience is several orders smaller than football's, so there is not enough money to build shot-tracking systems. The second is cultural: British billiards takes more pride in a player's character than in their metrics, so the media clings to narrative over tables. The third, the one I lean toward most, is habit: we never had the tools, so we never formed the demand, so we never asked.

The difference between these three explanations matters in practice. If the cause is economic, the gap will fill itself as the sport grows. If it is cultural, it will persist. If it is habit, it can be broken by a small group doing the right work. I do not have enough data to choose, and I say so plainly rather than pretending certainty.

So what could change? Look at football: the xG revolution did not begin with the big clubs. It began with bloggers, independent analysts, people who patiently logged every shot and assigned it a probability. They did that for years before clubs would pay for data. Billiards could follow the same path: a small group logging every shot at major events, building a process-metric set, and letting the market judge.

Another force could drive change faster than any newsroom: the betting market. When stakes are large enough, demand for process data surges, because bettors want to know who is truly playing well rather than who is merely winning. This is a driver I watch with caution, since it can produce good data but also pressure that distorts how the game is played.

I have started doing that, modestly. For every match I watch, I log the cue ball position at the start of the shot, the shot type, and the result. The dataset is still small — a few hundred shots — and I know its limits well. With a sample that size, I only dare speak to trends within individual matches, never to generalise across a season. Three hundred shots, three ways of classifying them, and a single question: can a shot-quality metric exist that results alone cannot see?

Data limitations: This piece rests on personal observation and public outcome data from World Snooker Tour. No process dataset has been published for verification, so every claim about shot quality remains a hypothesis. My self-collected sample is far too small to extrapolate. I will update it as next season's data arrives.

The miracle of a turnaround shot is not magic, but a series of small decisions made deliberately — and those decisions deserve to be measured, not merely praised. A sport's journey is not an upward arrow but a scatter plot. Billiards is still waiting for its first data points to be plotted, and I intend to be one of the hands drawing them.