The Spreadsheet That Did Not Lie: Cricket's Chain of Evidence

I begin with an empty cell. On a January morning in 2026 I opened an analysis report: eight sections, eight questions, and the same line returning in every answer — insufficient information, assessment not possible. No team. No player. No format. No score. Only one word hanging there: cricket. I have worked with numbers for eleven years, so I know these empty cells are not a failure. They are a warning. A number with no lineage is not evidence. At eighteen I bought a nine-pound notebook. Through Tranmere Rovers' 2026-18 season I charted forty-six National League matches by hand — 1,214 shots, each logged with distance, angle, body part and defensive pressure. Nobody paid me a penny. Everyone explained the club's promotion run with "momentum." My sheet said something else: after January their expected goals per shot had risen by 0.04. In May 2026 they beat Boreham Wood 2-1 at Wembley. I stopped writing "deserved" and started writing counts. Cricket is walking the same road now. Every broadcast carries expected runs, win probability, pressing indices. Graphs on the replay screen, arguments on social media. The question stays: where did these numbers come from? Who charted them? What sample size? What cut-off date? A spreadsheet becomes evidence only when each cell is reconciled against the one before it. This is where the idea of a blockchain earns its place — the chain. Every true number is bound to the previously verified number. Loosen one cell and the whole account tilts, exactly as one bad block makes an entire ledger untrustworthy. That is what I do — verify by hand. I trust a model's output only after I have reconciled it myself. The method is simple but needs patience: define the sample, isolate the variable, verify by hand, then let the anomaly revise the accepted explanation. In cricket this is harder than in football, because the outcome of a single ball depends on pitch, wind, dew, light and the batter's mind — each a separate variable. If someone pushes one broadcaster's single figure as final truth, that is not a chain, that is centralised belief. Cricket is soaked in the temptation of numbers, because the game itself is full of statistics. Powerplay, middle overs, death overs — each phase carries a different meaning. But those phases become meaningful only when pitch, opponent and conditions are held fixed. If a batter's death-over strike rate is always earned on easy pitches, that is not proof of his skill but of his advantage. Beside every strike rate I write down: what the pitch was, which bowler, which innings. Otherwise a number is passed off as truth, and we keep getting the right answers to the wrong questions. At the 2026 World Cup in Russia I was nineteen. In a room in Liverpool I watched all 64 matches and logged every minute. Croatia's knockout run was 120, 120, 120, 90 minutes. France's was 90, 90, 90, 90. The match clock was clear: Croatia would be physically spent in the final. Four hundred and fifty minutes against three hundred and sixty. France won 4-2. I pitched the piece to a new-media outlet, it ran, and a commenter asked whether "the girl" had actually watched the games. I answered with the match-clock data, not with my feelings. The piece did forty thousand reads. The spreadsheet did not lie; it waited for me to catch up. In the spring of 2026 the game stopped, then returned to silence. For my master's research I hand-coded all 81 Bundesliga matches played after the May restart — crowd presence, referee decisions, added time. The home win rate was 43.3 per cent before the shutdown and fell to 33.3 per cent after it. I did not tweet it; I made it a dissertation chapter. The sample was small, the effect size modest — which is exactly why I trusted it enough to build on. Eighty-one empty stadiums taught me that home advantage is partly noise. With the crowd gone, roughly ten points of it vanished. A large part of what we call "pressure" is really human presence, the roar, the referee's subconscious. I no longer treat context as atmosphere; context is a variable. In the summer of 2026 I coded passes allowed per defensive action for all 51 matches of Euro 2026. Italy's press was the tournament's tightest — 8.4. Across seven matches they conceded only four goals and scored thirteen. I published the dataset with the method attached. A recruitment firm in the North West offered me a junior data role off the back of it. I took three weeks, asked for the job description in writing, and accepted on a six-month probation. My byline became a reliability signal rather than a personality. I am now a transfer market administrator. Here every rumour stands as a row, and every row waits for confirmation. At the 2026 Indian Premier League auction Sam Curran set a record at 18.5 crore rupees; the next year Mitchell Starc broke it at 24.75 crore. I write those numbers only when the auction papers reconcile. A transfer is not a rumour; it is a row of cells awaiting confirmation. In the market for young talent, that verification matters even more. Big clubs and big franchises now turn small-league prodigies into "satellite assets." They cover the gaps in their own academies by buying a player from a distant small side, while dodging the duty to develop locally. A young cricketer in a small league stops being a player and becomes an asset, priced by expected runs and an age curve. The greatest risk in this market is the misuse of numbers: one bright season average makes someone a millionaire, while another is ignored because nobody reads the context. An analyst who writes down the limits of the sample does not merely protect the player from fraudsters; he protects the investor too. This is where I object. People think a verified number means a true conclusion. It does not. My 81-match sample is small, the difference modest, and I have found no replication in leagues outside Germany. Correlation slips easily into the game — two series rise together and it looks as if one pulls the other. If a model says one thousand two hundred fourteen shots, I check the next one. And if the data calls itself zero, that too is a result — often the most honest one. An analysis afraid to say "insufficient information" is not analysis; it is deception. There is also precision theatre: two decimal places do not make a number reliable. I print a method note — source, sample, cut-off date — because what can be verified can be argued with; what cannot be verified only offers a chance to attack the person. Next season what I look for is not precision but provenance. Who is charting, what they omit, how long the sample runs. A number that can state its date of birth will survive. And the analysis that can admit its own emptiness is the one the next season's trust will be built on.