Nine Dimensions, Zero Verified Inputs: Indian Football's Problem Is Discipline, Not Data

There is a file on my laptop. It has nine sections — tactical-technical analysis, club finance and the transfer market, results and the public-opinion cycle, league landscape, rules and compliance, management and dressing room, risk profile, media narrative, and industry transmission. Every section has tables. The tables have cells. And almost every cell carries the same entry: N/A.

Nine dimensions, more than thirty rows, and not one verified number — no xG, no PPDA, no transfer fee, no league position. The document's own conclusion was that the problem lies not with football but with information. This is the moment an analytical framework puts its own inputs on trial. In Indian football that moment arrives every week; we simply don't log it.

Across the 2026-12 I-League I hand-coded 26 Pune FC matches from the stands at the Balewadi Sports Complex. Graph paper, a ruler, a stopwatch. That season produced 1,140 possession sequences. One line from it still forms the spine of my work: Pune FC conceded 60 percent of their goals within eight seconds of losing the ball in their own half. That number did not come from a dashboard. It came from a notebook I filled by hand, and I can still tell you the sample size behind every rate in it.

There was no public event data in Indian football then. There is now — or at least there is the claim of it. Camera tracking on every ISL match, valuation models in the transfer market, 'data-driven recruitment' as vocabulary inside clubs. What has not changed is the habit of audit. Nobody asks who filled those dashboard cells, from which matches, and after how many games they deserve trust. That gap is not a data gap. It is a discipline gap.

A nine-dimension framework is not bad in itself. What is bad is that it runs without inputs, and running without inputs it still produces a document that looks confident. The same notebook carried Jeje Lalpekhlua's reception points and a match-by-match record of how high Derrick Pereira's back four pushed. Both cells were filled, so both produced findings.

In 2026 the ISL arrived and FC Pune City signed David Trezeguet, 36, on a marquee deal. The press pack covered the arrival dinner; I counted his minutes from the Balewadi stands. Eight matches, 613 minutes, two goals, and 31 touches inside the box all season. In a four-part Marquee Report I set his output per minute against the club's Indian forwards. The gap was far smaller than the wage bill suggested.

This does not mean Trezeguet was a poor player. It means that to fill the cell marked 'marquee' I had to count 613 minutes. In a document where that cell stays N/A, no verdict emerges either — and that is the correct behaviour.

In 2026 India hosted the FIFA U-17 World Cup. I followed England from Navi Mumbai to the final in Kolkata, where they beat Spain 5-2. I coded Phil Foden's 61 receptions across seven matches — 44 of them inside the touchline, in the left half-space between the halfway line and the box. That map became a six-part geometry series. India lost all three group games; I wrote their shape without excuses.

Russia 2026 gave me 64 matches and 169 goals. Working nights from Pune on broadcast feeds, I tagged the pass before the assist for every goal — 118 pre-assists in all — and coded the origin zone. Sixty-one percent began in the half-space. France beat Croatia 4-2 in the final, and nine of their pre-assists came from that same right-side channel.

All three exercises share one thing: every rate was published with its n. Twenty-six matches, seven matches, 64 matches. Where there was no n, I wrote that there was no n. And where there was not a single match of data, the nine-dimension framework also answered correctly — 'unknown'. The problem is not the answer. It is presenting that answer as a finding.

Now let me state the mainstream view in its strongest form: India has too little football data. That is true. Against comparable leagues we have fewer public event datasets, fewer Opta-style feeds, less independent research. Those demanding a data revolution are not wrong.

But the reversal is this: a framework with nine dimensions and zero inputs is worse than a notebook with one dimension and 26 matches. A notebook cannot lie — what is written in it, I saw; what is not written, I did not see. A dashboard presents an empty cell as analysis, and the reader mistakes it for depth.

My condition is simple: if a club's recruitment model cannot state the n behind any of its conclusions, that model is not a decision tool but a presentation tool. And if every cell in an analytical document reads N/A, the correct conclusion is not 'risk unknown' but 'I did not collect the inputs'. The distinction looks small. It is the whole game.

I will withdraw that view when an Indian league club publishes a recruitment report in which every rate carries its sample size and its failure conditions. Until then, the notebook stays open. And the next time you read a 'data-driven' claim, ask one question: what is the n? If there is no answer, ignore the number — the claim is just as hollow.