The Numbers That Arrive Without a Format

Last year, a single-page printout landed in the press box at Mirpur's Sher-e-Bangla Stadium. A "stat sheet" forwarded by supporters before a Dhaka Premier League match. Beside a young batter's name: average 34.6. None of the eight of us sitting there could confirm whether that was a first-class average, a List A average, or a domestic T20 average. The match began. The number travelled through WhatsApp groups. By evening, a television panel had turned it into proof of a return to form.

That evening it struck me that the most dangerous number in cricket is not the wrong one. It is the one that is correct but carries no format label.

The story begins where the spreadsheet ends. Because the sheet may be finished, but outside a stadium is still awake—a curator watering the grass at three in the morning, a ticketing manager counting empty seats, a young batter walking to the crease with the weight of that 34.6 on his shoulders.

Context: one feed, four kinds of buyers

Cricket data now serves four distinct markets, and each wants something different. Broadcast graphics want speed and story—you have one over to hold the viewer. Fantasy and betting-adjacent derivative markets want second-by-second granularity. Franchise auction rooms want projection—how many wickets will this bowler take across the next three seasons. Boards and domestic structures want long-run trend—where the talent supply is thinning.

The problem is that all four often pull from the same feed, and that feed's domain label is far too coarse. Under a single umbrella called "cricket" sit five-day Test matches, fifty-over ODIs, T20s, The Hundred—even franchise leagues with different balls and different fielding regulations. No metric travels safely under that umbrella. Format is the first gate of analysis. Fail the gate and everything beyond is arranged furniture: fine to look at, useless to sit on.

I went looking for the deal and found the person behind it. It works the same way here. Behind an unlabelled number sits a scout who has been cutting video all night, waiting for a decision.

Core: where the numbers take a wrong turn

First, strike rate's two lives. A strike rate of 150 is elite in T20; in Test cricket the same figure carries no analytical meaning, because the length of the innings is a different game entirely. A bowler's economy of 8.5 is unacceptable in ODI cricket, yet roughly par in a high-scoring T20 innings. Average, strike rate, economy—use those three words without a format and the analysis starts walking in the wrong direction. Across ten years of watching matches I keep one small habit: in the notebook beside the scorecard I write the format first, then the number. Reverse the order and I lose count of how often I have got it wrong.

Second, an auction price is never a certificate of sporting quality. At the IPL auction on December 19, 2026, Mitchell Starc joined Kolkata Knight Riders for 24.75 crore rupees; at the same auction Pat Cummins went to Sunrisers Hyderabad for 20.50 crore rupees. At the auction on December 23, 2026, Sam Curran joined Punjab Kings for 18.50 crore rupees. What do these figures actually measure? Purse size, retention rules, the existence of a Right to Match card, the overseas quota in the XI—and scarcity at specific positions. Left-arm pace, wicketkeeper-batter, finger-spin all-rounder: supply is thin in those three categories, so the price climbs. An auction price and international cricket strength are two separate ledgers, kept in two separate books.

Third, the lure of the small sample. A single season of T20 explosion converts into international output at a low rate—not a new observation, yet it is resold under a new name every year. The historical hit rate of "next Tendulkar" or "next Kohli" framing is close to zero. Still, an 80 off 40 balls becomes the centre of national conversation the next morning, because the clip exists and the context does not.

Fourth, the home-away differential. Spin on Indian soil, swing in England, pace and bounce in Australia—assess any team or player without that geography and the evaluation stays incomplete. But if the venue is unnamed, the differential itself disappears, and the analysis starts looking five-star.

Fifth, the quietest defect of all: the empty template. Some months ago I was shown a twelve-tab analytics deck. Every tab formatted, every chart coloured, every heading filled. But one column, row after row, read: no data. Anyone skimming quickly could believe the analysis was complete. That is precisely the danger. A report that looks full while being hollow does more damage than an openly wrong one, because error gets caught and emptiness does not. The human cost is concrete: a young player can lose an opportunity on the strength of an unlabelled average, or be bought at a premium for the same reason. The ledger says profit; the terrace says something else.

This is where domestic cricket's arithmetic enters. At a Dhaka Premier League match, three hundred spectators, three names on the sponsor board, no broadcast. In the language of numbers, a failed event. But an empty stadium still has a voice if you listen. Among those three hundred sits a local coach who has spent seven years correcting one boy's action. That labour has no column in any dashboard. In 2026, when matches in Kolkata were being played to empty stands, I started a spreadsheet on Indian club finances. A few months in, I realised the income and expenditure cells were filling up while the cell for the people inside the loss sat empty.

The contrarian question: is the problem a shortage of data or a shortage of labels?

The conventional view is that more data improves decisions. My experience says otherwise. Data volume has multiplied over the past decade; the quality of professional commentary has not kept pace—because labelling discipline has not. The more numbers arrived, the more of them circulated without a format tag. As a result, "data-driven" is now often a misdescription; most of the time data is not driving the decision at all, the decision was made first and data is decorating it.

That is why the rarest skill in this trade is refusal. The analyst who says without hesitation, "the sample is too small, I will say nothing," adds the most value. I hold a contrarian view on auction prices: the price may be the most honest number in cricket, because someone put their own money at risk. Honest is not the same as correct—and an auction analysis that misses that distinction ends up as drawing-room argument.

Takeaway

Over the next two seasons, more franchise and board decisions will move into models. That much is certain. So the question is not technological. The question is whether the model pricing a bowler has ever watched that man bowl the fourth over after tea on a fifth-day Mirpur pitch. If it has not, what exactly is it pricing?