The Blank Memo: The Cricket Data Nobody Ever Tagged

The memo came back on a Tuesday. Eight sections, thirty-three rows, and one word in every row: insufficient. No title, no source, no core argument, no list of information points. The template itself survived intact — all the scaffolding, none of the building.

Four years before I ever thought about writing this way, I filed a three-page note from a tagging outfit's third floor in Hyderabad. It pulled every Rashid Khan delivery from the 2026 Under-19 World Cup and Afghanistan's 2026 World T20 legs, weighted wicket probability by the quality of the batter at the other end, and recommended an 18-year-old with a base price of fifty lakh. Sunrisers paid four crore. He took seventeen wickets at 6.62 and won Emerging Player. The memo was four pages; the market only ever read the fee. SRH still lost the Eliminator.

The difference between those two documents is not effort or skill. It is raw material. One had tagged deliveries behind it. The other had nothing, and I can tell you precisely why.

The three possible failures

When an extraction layer returns empty, one of three things has happened.

One: the source genuinely had nothing in it — an opinion with no number, a claim with no verification, a news item with no fact. Two: the material existed but the extraction layer never reached it — language, format, or access blocked the path. Three: the material existed and nobody had tagged it, which means it was produced in the part of the game where no one logs ball by ball.

The third case is a Tuesday, not an exception. A four-day Ranji Trophy match generates more deliveries than five T20s, and the tagging operator allocated to it is usually one person, usually the scorer, whose job is the scorebook and not wicket probability. Vijay Hazare and Syed Mushtaq Ali get cameras, but the field-placement and ball-track layers die the day the broadcast dies. A match that never reached television has no data anywhere.

Nobody had tagged him, which is not the same as nobody had seen him. That line is the spine of my oldest file, and it applies to a blank memo as much as to a blank player profile.

What tagging actually costs

A broadcast match has three data layers. The scorebook — runs, balls, wickets, names — is public and free everywhere. Ball-tracking — pace, line, length, swing, spin, bounce, wicket probability — needs cameras and software, so it exists only where broadcast rights were bought. The third layer is the expensive one: event tagging. Field placement, shot type, batter intent, pressure state, the yorker attempt that became a run. Humans build that layer, and human hours cost money.

A franchise with a four-crore purse can buy the third layer. A state association that cannot find two lakh cannot. The consequence is not that poorer cricket has less talent. The consequence is that our claims about poorer cricket are weaker — and we have learned to mistake that weakness for a property of the game.

Rashid in 2026 worked because his deliveries were tagged: two broadcast tournaments, two clean datasets. The model did not succeed; the raw material did. Had the same recommendation been needed for an Under-19 bowler in Bihar, I would have sent a blank memo, because I would have had not one tagged ball.

Two cases where the data spoke first

May 2026, Wankhede. Sunrisers made 178/6, Shane Watson's unbeaten 117 off 57 finished it, and Chennai won by eight wickets. My post-final breakdown landed on one number: between overs seven and fifteen, SRH scored roughly 24 runs below par for the phase, and that phase decided the match. Building that claim needed shot type, fielder position, and the expected return of each state — the third layer, available only because it was a final.

That same summer, with the calendar dark for the Russia World Cup, a digital outlet commissioned five explainers translating football's xG into cricket's Expected Runs. I filed four and missed the fifth deadline twice while recalibrating a boundary weighting nobody had asked for. It eventually ran as a footnote to the fourth, which I still consider the most honest thing I have published. Five explainers went out that summer. Four came back filed. The fifth is why I write.

September 2026, IPL in the UAE. Bhuvneshwar Kumar broke down after four matches and Sunrisers' season hinged on a replacement. I built a value model on yorker length and death-over economy and argued for T Natarajan, who took sixteen wickets. SRH finished third and lost Qualifier 2 to Delhi by seventeen runs.

Separately that season, I compared 2026 and 2026 venue by venue and found home advantage had flattened into noise without crowds. The crowd left, and the home advantage left with it — that was the finding nobody wanted, because it reframes every home record as a question about pitches versus people.

Date-stamp the model

Since the Impact Player rule arrived in 2026, a bowling average and a strike rate no longer measure what they measured in 2026. An all-rounder's value changed because his batting and bowling now sit in a twelve-man structure rather than an eleven-man one.

Date-stamp every model. Run the same query on pre-2026 and post-2026 samples and report the discontinuity instead of smoothing it away. I did, and several middle-order batters show an improved effective average after 2026 because there was an extra batter beneath them, not because they batted better. On the old model I would have sold that as progress. It is a measurement artefact, and the trap of trusting your own metric after the rules moved is the one I fall into most easily.

The access economy

Indian cricket's information flows downhill. The board owns broadcast rights, and therefore owns tagging permissions. Franchises run an embargo culture in which injury status, selection thinking, and fitness data are all internal. The data desk gets what already appeared on screen.

Evidence only becomes evidence to the extent it is permitted to be shown. My 2026 home-advantage work was a half-result: the effect was visible, the mechanism was not. Nobody publicly measures how crowd pressure bends an umpire's decision. So I published the correlation and stated plainly that the causal chain was unproven, and later cut the paragraph that would have made my strongest finding my weakest — the one headed 'what would change my mind.'

The counter-intuitive turn

An empty dataset is not an empty story. The real danger is that someone fills it. Dressing-room colour, an overheard selector's remark, a memory — this is the exact soil in which milestone narration grows. The fiftieth hundred becomes the story; the required rate at the moment the game turned gets buried.

Filling a blank with narrative is not the crime the reader pays for — the cost is that the reader never learns there was a blank. I have done this. Corollary: correlation read as causation. Empty stadiums and vanished home advantage happened together; that is co-occurrence, not proof. The mechanism likely splits across pitch, travel, weather, and unconscious umpiring bias, and anyone who reduced it to crowd presence wrote a better sentence and a worse conclusion.

I keep a file of things the data said first and the pundits said later. It also records how often the data was wrong and I merely looked right. Nobody prints that half.

What to track next

Three signals, none of them scoreboard.

Permanent ball-tracking for domestic fixtures. The day a state association gets a standing optical arrangement, the geography of Under-19 scouting changes; until then every claim about untagged cricket stays conditional.

The survival of the Impact Player rule into the next mega auction. If it holds, 2026 is a structural break and my models need rewriting. If it goes, 2026-26 is a bracket to exclude from samples rather than smooth into them.

One franchise publishing its own scouting ledger. That will be the clearest signal yet that talent identification has been privatised away from zonal selectors — and the next Rashid will surface in a franchise tag before he surfaces in a commentary box.

I still do not have an answer to this week's memo. I do know why it came back empty: the cricket it described was never tagged. So the next time someone asks how good a player is, the first question is not his average. It is how many balls were recorded.

Every market has a blind spot; mine is that I still check it twice.