From Empty Datasets to Blockchain Verification: The Question of Credibility in Cricket Analytics

Last week an analytical report landed on my desk. Eight sections, every result identical — insufficient information, cannot be assessed. No title, no source, no information points. Only emptiness. As an analyst, a null result is nothing new to me; the question is new: when the base layer of the analysis is blank, how much of what is built on top of it is true?

I have watched cricket for fifteen years, from Sylhet. Staying up for matches, saving screenshots, arranging ball-by-ball data into tables — that is my work. This experience taught me one ordinary but neglected truth: the scoreboard does not lie, but the interpretation of the scoreboard often does. And the biggest enemy of interpretation is not an opponent — it is empty data.

Modern sports analytics runs on two layers. The first is data capture — who faced how many balls, what happened in which over, where each delivery pitched. The second layer assigns meaning to that data. The problem is that when the first layer is blank, the second layer begins filling the gap with imagination. In technology this has a familiar name — garbage in, garbage out. The report that reached my desk did not fall into that trap; it honestly admitted it held nothing.

But that honesty is rare. In the real world we often place assumptions where blank data should be, and serve those assumptions wrapped as facts. The cricket market is full of this — declaring a player's future from a small single-match sample, treating domestic form as an international benchmark, explaining results while ignoring the toss or the Duckworth-Lewis effect. The source of these errors is not a lack of talent but a lack of source verification.

In the Bangladesh context this gap is sharper. Our pace bowlers' workload, the distance between domestic and international seasons, the nature of Sylhet or Dhaka pitches — when decisions are made on these, reliable, time-stamped data is conspicuously absent. We often decide on old memory or celebrity opinion, because the verifiable data layer is not in our hands. This is where blockchain becomes relevant — not in the form of cryptocurrency, but as source documentation for data.

Blockchain rests on three ideas: decentralisation, immutability, and universal verifiability. In the context of sports data, these have concrete meaning. If ball-by-ball data is once written to a distributed ledger, no one can quietly alter it. Who supplied the data, when, and from which source — all of it is linked in a chain. Conditional rules can be set through smart contracts — for instance, if a data source is not verified, that data simply cannot be used in analysis.

I remember 2026. I was writing a long analysis of Monaco's 4-2-2-2 shape — 107 goals in a season, 30 wins in 38 matches. You have to start in the half-space, because that is where Monaco's goal machine hid. Using free screen-capture tools, I measured the passing lanes and showed where the attacks came from. I did not write a single line without verifying data. Had that verification layer not existed, I might have written beautiful prose — but not the truth.

In cricket this verification layer is called DRS — the Decision Review System, introduced in 2026. Ball-tracking, UltraEdge, Snicko — together a parallel information infrastructure that allows an on-field decision to be challenged. Notably, this system did not replace the umpire; it added a layer for verifying the umpire's decision. Blockchain can be exactly such a layer for sports data. Here is a subtle but crucial distinction. DRS asks — did this ball pitch outside leg stump? Blockchain asks — who supplied this data, and do they have the standing to tell the truth? The first is the truth of an event; the second is the truth of a source.

On 14 July 2026, at Lord's. The World Cup final was tied, and the Super Over was tied. The title was ultimately decided on boundary count — England 26, New Zealand 17. One rule, one number, one World Cup. That night I realised that a match's outcome is decided as much by the source we accept as data and the rule by which we interpret it as by the players' skill.

Now the question is how reliable those sources themselves are. If a World Cup final's outcome is decided by a number, and the place where that number is recorded can be altered by someone, what then? This is where blockchain brings immutability to data. Imagine every ball of a cricket league recorded on-chain. From the ball-tracking system on the field to the scorecard, every step carries a time-stamped signature. Who supplied it — a match official, an automated camera, or a third-party data provider — becomes clear. If a dispute arises after the match, there is no need to rewrite history; simply searching the chain shows who supplied what.

Here there is a clear commercial reality. Writing data to a blockchain costs money, costs speed, and most of all costs privacy. How wise is it to put a team's internal performance data on a public ledger? A possible answer: keep the raw data private but place a cryptographic hash of it on-chain. Privacy is preserved, and verification remains possible. If a player's injury history and workload limits are written into smart contracts, injury insurance and selection decisions become verifiable.

A well-known weakness of blockchain is the oracle problem. The chain cannot see the outside world on its own; it must be given data. In cricket, who is this oracle — the ball-tracking camera, the scorer, or the umpire? If the oracle itself errs, the chain's immutability hardens that error. In other words, the security of the data chain ultimately depends on the honesty of its entry point.

Yet here I disagree with blockchain enthusiasts. Technology does not make bad data good. If the first-layer data is wrong, blockchain will keep it immutably wrong — forever. Immutability then is not a virtue but a curse. If a wrong number cannot be changed, the path to correction is closed. Cricket's data is alive — the meaning of an innings sometimes changes in the next match; a static ledger cannot capture that nuance.

I remember 2026 — empty stadiums, Bayern Munich beating Barcelona 8-2 in Lisbon. Bayern took 26 shots, Barcelona 7. I was then writing about pressing triggers and the acoustic vacuum of silence. That day I understood that present spectators and absent spectators are both data, but each demands a different interpretation. Likewise, immutable data and mutable data are both needed, for different tasks.

The real problem is not technology but incentive. Who supplies data, why they stay honest, what the penalty is for a detected error — without settling these, blockchain is merely an expensive ledger. One more thing must be remembered: data verification and the truth of a decision are not the same. I have long held a view on VAR — the phrase clear and obvious error is itself unclear. Two umpires can reach two decisions from the same replay. Blockchain can say who supplied the data, but who will interpret it — that human, person-dependent gap remains.

And this is where a favourite idea of mine applies — the cages Matuidi built are not visible on the scoreboard. In the 2026 World Cup final France had 39 percent possession, Croatia 61. France had 6 shots on target; Croatia managed only 3 from 15 shots. Yet France won, because the invisible work — the defensive winger's role, the machine that shut down build-up — does not show up in numbers. Blockchain performs exactly that invisible work for sports data; it does not score goals, it verifies the source of the goal.

So that blank report on my desk is not a failure to me but a warning. A system that can admit it lacks data at least does not lie. The danger comes when blank cells are filled with clever prose. When I watch the next match, I will notice something new. Not on the scorecard, but in the data chain behind the scorecard. Who is supplying the data, at which layer it is verified, and where a gap remains. Because in the end cricket too is a search for truth, and the first condition of any search for truth is one thing alone — the data must be real.