The Silence of an Empty Spreadsheet: When Asian Cricket Data Goes Missing

Last week, in a small work room in Rangpur, I ran an old model again. In 2026 I had hand-collected 3,410 shots from 132 Bangladesh Premier League matches; the cells were full. This time I opened the file and the cells were empty — not a single row, not a single value.

Many would call that a model failure. To me it is something else. Over more than twenty years I have learned that an empty spreadsheet is never neutral. A blank cell still speaks. So the question is not 'what is the data saying' but 'who created this silence, and who benefits from it not being seen'.

The file I opened carried one label: Asian cricket. That was all. No title, no source, no analytical point. Only the region. And that is exactly where today's story begins.

Cricket is played more in Asia than anywhere else. Across India, Pakistan, Sri Lanka, Bangladesh and Afghanistan, thousands of matches are played each year and hundreds of millions watch. Yet much of the data we use to measure the game is built outside the region.

I was lucky in one sense: in 2026 I had no public xG, because nobody had built one for that league. So I set the weights myself and hand-coded distance and angle. That was my first lesson from the Rangpur spreadsheet: when public data does not exist, who makes the decision? Whoever holds private data.

Today the main sources for Asian cricket are the ICC's official scorecards, ESPNcricinfo, Cricbuzz and platforms such as CricSultan. They give match results, strike rates and economy rates — but they do not measure pressing structure, the reasons behind field placements, or the story of a return from injury. Those gaps are my actual work.

I watch the game twice: once with the eye, once with numbers. The habit began at Russia 2026, when I watched Germany twice, once with naked eyes and once measuring PPDA. Their PPDA drifted from 8.9 in qualifying to 12.6 at the World Cup. The number whispered that their press had decayed. Yet my model still ranked them third favourite. I hedged in the text and lost the argument anyway. From that loss I learned to keep a quiet appendix alongside every loud thesis — a place where I write, openly, where my model went wrong.

Empty data has three faces, and I have learned to tell them apart.

The first is random missingness. The data exists; a few cells were accidentally left blank. A camera turned a second late; a scorer forgot a ball. It does not destroy analysis, it just adds noise.

The second is structural missingness. This one is dangerous. A whole series, a whole league, or a whole tournament dimension is systematically unmeasured. Domestic cricket in Asia has almost no fielding data, because fielding needs expensive tracking that nobody wants to fund. So we know a batter's runs but not a fielder's true value. Who is quick, who takes the right angle — all invisible. Scouts then fill the gap with eye impressions, and eye impressions carry bias.

The third is deliberate missingness. This is my most uncomfortable finding. Nothing was lost; someone chose not to look. A club never publishes the sprint numbers of a player returning from injury, because it could hurt his market value. The gap is no longer innocent; it is protection, a bargaining tool.

A week after my first piece was published in 2026, three betting syndicates emailed me. They wanted my method, not the result. I understood why later — the cells I had filled were the cells that were dark to them. When public data is absent, the advantage moves to whoever builds their own. In Abahani Limited's title run I found a 9.4 gap between their actual goals and my model's xG. The number was saying something about a team's fortune that the naked eye could not see.

I opened a blank spreadsheet and let the Bangladesh Premier League teach me. The league told me where information exists and where it does not. A model is like a monastery: you enter to escape the noise, then hear it more clearly inside.

What does that clarity give us? The big decisions in cricket — squad selection, auction prices, the valuation of returning players — are often made on incomplete information. When a coach says 'there is something about this boy', he is really filling a blank cell with his own eye experience. That is not wrong, but it cannot be measured, and unmeasurable things carry crore-scale decisions.

So when an analysis pipeline returns nothing, I do not discard it. I ask: what kind of missingness is this? Did someone forget to supply the data, or choose not to? That question is my missing-data forensics — a crime-scene investigation where you look not for the data's fingerprints but for the fingerprints of its absence.

In Asian cricket those gaps are wider, because the balance of power sits here too. Whoever controls the data also controls the narrative. Who stays honest about injuries, who sends a half-fit player out to sell drama — that is an editorial decision, not a data decision. I have seen many rushed returns from injury; the body heals, but the fear in the head never shows in a number, and that is exactly where the second act is lost. Where the data is empty, nobody measures the risk.

What an empty spreadsheet taught me is this — silence is not zero; it is a new baseline with its own residuals.

But here is my biggest warning, and I aim it at myself. Seeing a blank cell and calling it a mysterious truth is my easiest sin. Missing data must not be romanticised. A blank cell is weak data, not a moral message.

There is another trap: correlation is not causation. A player's low sprint count does not mean poor fitness. Perhaps he stood in the right place, so he did not need to run. Matching number to number without the story inside the game means building the wrong story. So before every conclusion I check the base rate, state the sample size, and add at least one eye witness — because a number is a lens, not a verdict.

So the question remains — in the next round, who will supply the data and who will not? Those who do will read the market first. Those who hide it will ensure nobody ever knows the true price of their players. For me the answer is clear: let the data born on the field reach the field first. The rest, we will watch through an empty spreadsheet.