Empty Columns, Full Stadiums: A 41-Field Audit of Asian Cricket’s Data Registry

On the evening of 2 November 2026, at the D.Y. Patil Stadium in Navi Mumbai, India beat South Africa by 52 runs and won the Women's World Cup on home soil for the first time. I was in the third row of the press box, and the scorecard open in front of me had 41 columns. Thirty-nine were filled that night. Two were blank: attendance, exact, and broadcast minutes by language.

Three weeks earlier, the same scorecard for a domestic women's one-day match had 12 columns filled. Twenty-nine blank.

The difference was not the cricket. The difference was the number of people paid to fill the columns.

I opened the women's files first. That has been the habit since 2026, when a credentialing officer told me that women's matches 'didn't need a stats desk.' That year at Derby, Harmanpreet Kaur made 171 not out off 115 balls, India posted 281 for 4, and Australia lost by 36 runs. I coded all 115 deliveries — phase splits, boundary percentage, dot-ball pressure — in the same schema I used for men's internationals.

At the stadium that night there were more than 40,000 people, and not one column that could tell me how many of them were listening in Tamil.

A standard is a column you must fill, not a slogan

— Root: The 41-Field Schema | Scenario: opening a parity audit of the women's game

My schema has 41 fields and it is published field by field, so that any reader can rerun the audit. It carries innings phase splits, run-rate context, boundary percentage, dot-ball pressure, bowler spell-phase load, catch difficulty band, pitch report, dew, toss, umpires, match referee, travel days, rest days, attendance, and broadcast language.

The point of publishing it is reproducibility. If I say a match record is incomplete, the reader should hold the same 41 columns I do. Otherwise the sentence is an opinion, not a finding.

One clarification. This article is not about whether women's cricket is good or bad, or what its market value in Asia ought to be. It is about how much of the match actually lives inside the thing we call the match record.

Asia is the right place to ask, because three things happened at once. The Women's Premier League launched in 2026, and its media rights went to Viacom18 for five years at ₹951 crore — a figure that tells you the scale of money that arrived. India won the World Cup at home in 2026, which tells you the audience arrived too. And in September of the same year the men's Asia Cup was played in Dubai, which tells you the calendar was already full.

All three point one way: audience, money and calendar in Asian cricket have grown. The question is whether the registry grew at the same rate.

The dataset and its definitions

The audit window is January 2026 to November 2026. It includes every match played on Asian soil for which some public scorecard exists. That is 1,144 matches across six categories. My desk did the coding, and every match was asked one question: how many of the 41 columns were actually filled.

Definitions first. 'Filled' means a value a third party can reproduce without guessing. 'Estimated', 'approx.', and an empty bracket were not counted as filled. 'Televised' means live television or live streaming coverage, not highlights. That second definition carries the finding.

What the audit found: six categories, one slope

Men's internationals in Asia (312 matches): 38 columns filled on average. Women's internationals (148): 29. Men's domestic, televised (96): 33. Men's domestic, non-televised (402): 18. Women's domestic, televised (84): 25. Women's domestic, non-televised (102): 11.

The distance from top to bottom is 27 columns. No single cause produces it.

The obvious reading holds: women's records are thinner than men's. Across 810 men's matches the average was 27.5 columns; across 334 women's matches, 22.5. A ten-column gap is real and should not be minimised.

But that is not the whole picture. When I split the data by two variables — broadcast and gender — the slope divided. Televised matches averaged 35 columns; non-televised matches averaged 17. The broadcast effect is roughly twice the gender effect. The gender gap survives inside broadcast (33 for men's televised domestic against 25 for women's), but it is the smaller term.

The lowest cell is the interaction: women's domestic, non-televised, 11 columns. The emptiest record in Asian cricket belongs to the match that was neither on television nor played by men.

Ball-by-ball availability repeats the slope: 100 per cent in men's internationals, 94 per cent in women's internationals, 21 per cent in non-televised women's domestic fixtures. In four of five of those matches, no public record of a single delivery exists today. Only the innings summary survives.

And then the number that unsettles me most, which is not a large number at all. Field 41 of my schema is 'broadcast language.' It was filled zero times in 1,144 rows. Not once.

The column existed. The data was not hard to obtain — broadcasters publish language schedules weekly. It stayed empty because nobody owned it. A standard is a column you must fill, not a slogan — and a column without an owner stays empty.

— Root: The 41-Field Schema | Scenario: assigning accountability to an empty field

Bylines run the same way. Between November 2026 and November 2026, Indian English-language newspapers and digital desks carried 37 bylines on domestic women's cricket and 1,924 on domestic men's. A ratio near 1:52. I am not arguing that bylines must track viewership. I am saying that a match without a byline has a score, not a history.

The Croatia problem began where the broadcast data ended

At the 2026 World Cup I got the desk because the 41-field schema travelled. Across 64 matches I rebuilt xG and PPDA from raw event feeds. Three providers disagreed on what counted as a big chance, so I wrote one definition and applied it 1,580 times. My model gave Croatia a 4 per cent chance of reaching the final. They reached it.

I did not discard the model. I added columns for rest days, extra time and travel, and published the errata myself. Data does not lie — my schema did. That is the lesson I am now applying to Asian cricket.

Because a match outside the broadcast data is also outside the model. A batter whose every innings has phase splits can be measured. One whose doesn't gets overlooked — and the overlooking is later explained as a shortage of talent.

The empty stadium ledger

Fourteen days, one empty stadium, and a registry that had to breathe again.

In March 2026 the registry went dark and the board called me back, because an auditable ledger was suddenly the only thing anyone trusted. I rebuilt the IPL player-movement desk from Kolkata in 14 days: 8 franchises, 200 registered players, NOC scans, quarantine windows, a 48-hour clearance checklist for the UAE shift. While the league played to empty stands in Dubai, I logged 512 closed-door matches across football and cricket.

Two things emerged. Home win rate fell from 44.8 per cent to 40.2 per cent. And in IPL 2026 the toss winner took 61 per cent of matches.

I am not calling either of those causal. I am saying the crowd is a variable, and when it disappeared something else disappeared with it. The same applies to the women's registry: a variable that never gets recorded cannot be measured, and after a while people conclude it never had an effect.

What the schema cannot see

Here I have to testify against my own instrument, because a well-built instrument invites the belief that everything worth knowing fits inside it.

It doesn't. The schema does not weigh fielding — a dropped catch is logged, a run saved is not. Weather is a bracket, not wind. The crowd is a number, not an atmosphere. And most of all, the schema cannot see the bowler with a sore shoulder, the player travelling with a small child, or the teenager watching in Tamil commentary — because the language column was filled zero times.

Three things I could not measure in this audit, and I record them as unavailable: the part of bowling quality that doesn't show in the wicket column, fitness management at domestic level, and the language audience. An unavailability is also a finding.

The contrarian turn: the causality runs backwards

The convenient story is that women's cricket has less data because it attracts less interest. It sounds clean and it fits my dataset — if you put both variables in one row.

They are not in one row. Interest is measured after the data has been filled or not filled. The decision to fill it was taken months earlier, when someone was assigned to the match or not.

Emptiness and low interest are not the same column — they are the same sequence. First the column goes empty, then interest falls. A match with no ball-by-ball has no clips; no clips, no viral moment; no viral moment, nobody assigned next time. That is a closed loop, and it starts in staffing, not in appetite.

Which is why the post-WPL difference is so sharp. Where the broadcast team turned up, the columns got filled — 25 on average in 84 matches. Where it didn't, 11. The variable is not the players. It is the camera and the coder.

And if I had stopped the audit at gender I would have missed the larger term. Men's domestic non-televised fixtures averaged 18 columns — the Ranji Plate group and the north-eastern teams are recorded as thinly as the women's domestic game. Asia's biggest data deficit is not sorted by gender but by television. Gender intensifies that deficit; it does not create it.

I should add that the serious work here has been done by Indian journalists and analysts. Sharda Ugra's writing on the coverage politics of women's cricket is the frame this whole argument sits inside; Himanish Ganjoo and Vinayakk Mohanarangan set the standard for data-led cricket writing without which my audit reads as a hobby; and S. Giridhar built the tradition of reading a scorecard as history. My contribution is one more row, not a replacement.

Forward

One thing I will record about myself: I am writing this in the hope that the next audit is not mine. The 41 fields are public because a registry that exists only inside its author is a diary, not a registry.

Next season I need exactly one new column — broadcast minutes by language — and a name attached to it, the person accountable for filling it. Without the name the column stays empty whether the schema has 41 fields or 81.

And every scorecard should carry a number nobody currently prints: a completeness rate, stating how many of the 41 parts of this match are actually on the record you are reading.

The question is not whether Asian cricket has data. The money has arrived, and the data will follow. The question is whether a registry with 11 of 41 columns filled is a record of the match, or a record of our absence from it.

Next season's first question should not be how many people watched. It should be how many columns got filled.