Ledger of Truth: Blockchain and the Silent Crisis of Provenance in Cricket Data

The ground scoreboard read 84/3 after 12 overs. In the handwritten notebook on my lap in the Khulna press box, it read 87/3. A three-run gap — born of two boundaries and a leg-bye counted inside a single over. The live graphic, the broadcast, the next morning's newspaper statistics — all accepted 84 as truth. No one asked who counted the number, how they counted it, or where they stored the count. For me the problem was never the runs; it was the truth. Before the model had a name, I counted chances by hand; without a ball-by-ball transcript, that day's over could never be recovered. Data survives only when it carries a birth certificate. The bigger cricket has grown, the bigger its data has grown, the higher its market value has climbed — yet no one asks where that data's birth certificate is kept. Cricket data today moves in three layers. The first is the event on the field: a ball, a shot, a run, a field placement. The second is the recording of that event: who is writing, when, on what device. The third is the analysis born from that record: expected scores, pressure indices, per-90 metrics. The crisis is that the three layers live in different hands, different organisations, different formats. There is no bridge between the truth of layer one and the truth of layer three; no one can trace exactly where a number came from. This missing bridge needs a name: the provenance gap. When an analyst says "this batter's strike rate is 140," he is really quoting a number whose birth, age, and parentage no one knows. This is where blockchain extends a hand. A distributed ledger — one shared record spread across many computers — timestamps every entry, cryptographically links it to the previous entry, and makes tampering impossible. You cannot delete a number; you can only add a new entry, and that new entry carries the fingerprint of every entry before it. Imagine it: if every ball, every hand-counted chance, every correction had entered one shared, immutable ledger, the 84-versus-87 gap would not have been buried — it would have been visible. Who changed it, when, and why would all be on record. The weakest point of today's analytics industry is not the model; it is the provenance of the model's raw material. But here I must return to my old habit: first define the metric, then show the raw count, then adjust for environment, then deliver a verdict. Before we talk about blockchain, we must be clear about what we intend to write into the ledger. "Pressure" is the most abused word in cricket. The PPDA concept borrowed from football does not sit directly on cricket, because cricket's pressure is discontinuous — a pause after every ball, a pause after every over. So I define pressure not as a feeling but as events: the dot-ball cluster (four straight balls with no run), the wicket-taking ball (a delivery that takes a wicket or creates the chance), and boundary suppression (how far the fours and sixes were contained in an over). Count these three events and pressure stops being a mystery — it becomes a number. I have learned from football's models, but never blindly. In the 1-1 draw between Abahani Limited Dhaka and Sheikh Russel KC, my xG model gave Abahani 2.7 against Sheikh Russel's 0.8. The scoreline said draw; the model said finishing collapse. That gap between result and process taught me the lesson: the scoreboard is a sentence, the model is the transcript. The same logic applies to cricket. In 2026, analysing 83 post-COVID Bundesliga matches, I found that the home-win rate in empty stadiums fell from 43% to 33%, and goals per game dropped from 3.2 to 3.0. Out of that came an "empty stadium adjustment coefficient," adding 0.15 xG to away teams. I called four upsets before they happened. In cricket, the same work must be done with Dhaka's pitch, evening dew, humidity, opposition quality, and resource gaps. At Mirpur, evening dew disarms the spinner and arms the batter — a truth no model delivers unless we register the timing and effect of the dew beforehand. And this is blockchain's first practical application: writing the correction coefficient in advance so that it cannot be altered after the result is known. This is pre-registration — an old scientific discipline that, once placed on an immutable ledger, can never be withdrawn. Now to the raw counts. A Test match holds 300-plus balls, and behind each ball sit at least eight decisions — line, length, shot type, field position, run, over, bowler, batter. A T20 holds 120 legal balls, but every ball also carries field placement, DRS, reviews, dead balls, wides, no-balls — thousands of data points in all. If a single one of those thousands is wrong, and that error enters the next layer of analysis, the entire chain of decisions is contaminated. Multiplied out, one error can make a model wrong for a whole series. Muralitharan's 800 Test wickets, or Sachin Tendulkar's 100 international centuries — magnificent numbers, easily verified, universally known. But the story behind each wicket or each century — which pitch, which ball in which spell, which batter, at which moment of which innings — if that information is not timestamped in a shared ledger, the number is only a result, not evidence. We memorise results and forget evidence. Here I never treat my manual-count habit as morally superior to machine data. Hand-counting means calibration — opening your own eyes against the tracking data. I publish both and record the gap between them. Blockchain can automate exactly this gap-recording: every hand-count, every machine-count, and the distance between them, all in one ledger, on one timeline. This journey is not new. It began with the handwritten Wisden-era scorebook, moved to the official scorecard, then television graphics, then Hawk-Eye and ball-tracking, and now sensor-laden bats and smart balls. Each layer added information while also widening the distance from the source. Between the ball that happened on the field and the ball that returns to our screens as data now stand how many people, how many pieces of software, how many edits. A practical picture. Take a ball-by-ball data ledger. Each entry would read: ball number, bowler, batter, outcome, field map, scorer ID, timestamp, and the cryptographic hash of the previous entry. If anyone later tries to alter a single entry, the hash of the entire chain changes, and the tampering is caught. That immutability is no curse for the analyst — it is a blessing: no one can "correct" the data to fit the result. One lesson from my own work is relevant here. When the source material is empty — no name, no number, no match — the only honest answer is: "insufficient information, cannot assess." Filling a blank with a guess is analytics' greatest sin. A ledger can enforce this honesty technically: no entry is accepted without a source. Without a birth certificate, data has no existence. DRS shows us another uncomfortable truth. Ball-tracking, predicted path, ultra-edge — this data is private, protected, closed to the public. A decision emerges from information no spectator can verify. Blockchain can open that door: if every review's tracking path and every prediction's uncertainty sat on a verifiable ledger, "trust us" would be replaced by "verify it." The market beyond the field rests on the same truth. Fantasy leagues, betting exchanges, sponsorship valuation — all depend on ball-by-ball data, and all are blind to who supplies it. A tamper-resistant feed protects not just analysis but the integrity of the market. When data is money, the truth of the data is directly a question of price. In Bangladesh the stakes are different. Our league, our broadcast, our pitches — all relatively under-documented territory. If a standard for data provenance is built here, our voice in world cricket analytics becomes that of a producer, not merely a consumer. An immutable, verifiable dataset is itself an asset, because anyone can rely on it. But reality must be respected too. Writing every ball on-chain carries real costs in expense, latency, and storage. So the answer is probably not full on-chain, but a hybrid model: at the end of each over or session, anchor a cryptographic hash (a Merkle root) of all that phase's entries into the ledger. Cost falls, yet the evidence stays intact. Technology is a game of patience; for proof, each phase — not each ball — is enough. And here I must say it with a cold head: blockchain is no magic, and I will not step into the trap of environmental determinism. Building an immutable ledger of bad data still leaves bad data — you simply can no longer erase it. Immutability is not accuracy; a timestamp is not truth. If a scorer mistypes, blockchain makes that mistake immortal. Second, correlation is not causation. Dew and defeat may occur together, but whether dew caused the defeat is something a model will not tell you — good questions and good experiments will. Third, the eye test is a witness, not a judge; the model keeps the transcript, but it does not pass the verdict. Blockchain will keep the transcript intact; the verdict stays in human hands. Fourth, the reality of cricket data is its plurality. In the same match, two providers keep two different field maps and two different wicket classifications. A shared ledger will not erase this conflict — it will expose it, and that exposure is the real benefit. The task of evidence is not to hide conflict but to show it. The next round of cricket analytics will not be won by whoever owns the most complex model; it will be won by whoever owns the most honest evidence. Before the model had a name, I counted chances by hand — and today technology lets me store that hand-counted number in a way no one can steal, alter, or deny. So the question is simple: once every truth on the field is written into a ledger, who will be left to blame for burying the gap between result and process?