The Ledger Never Forgets: Home Advantage, Silent Rows, and the Case for an Immutable Scorebook in the BPL

Last Friday evening I was watching a match at the Zahur Ahmed Chowdhury Stadium in Chattogram — notebook in hand, laptop beside me, counting overs by the minute out of pure habit. For the first ten overs the home side played to a perfect script: 58 runs in the powerplay, two wickets, a dot-ball rate of 38 percent, a boundary ratio sitting exactly where my previous season's baseline said it should. What happened after the break stopped me cold. Between overs eleven and fifteen the home side's run rate fell from 9.2 to 6.1, three wickets fell, and the scoreboard called it "losing rhythm." My notebook said something entirely different: across those five overs the fielding side bowled 24 of 30 deliveries through spinners, and the line was outside off, a long way outside. The scoreboard was describing emotion; my rows were describing a decision. Building a trend from one match is foolish. So I opened the hand-coded season again, and the first thing I saw was that the margins disagreed — exactly as they always do. The first wall you hit working on the BPL is not technical, it is structural. I have coded four seasons of this league match by match — 462 games in total, logging for each one the ball-by-ball over number, innings phase, scoring rate, toss decision, pitch type, daylight or floodlight, and a rough estimate of crowd attendance. What keeps returning is a single question: how much home advantage really exists, and where does it come from? In the seasons where crowds were present, the home side's win rate across my coded BPL matches came to 43.7 percent. In the 2026 matches played in empty stadiums, the same calculation fell to 37.9 percent. A gap of six percentage points. At first glance the story looks simple: crowds return, home advantage returns. But fourteen months of silence taught me that an empty row is not a zero, and a gap between two numbers is not automatically a cause. When I sit down to reconcile the columns of those 462 matches by hand, what surfaces is that the empty-stadium season was simultaneously the smallest sample and the dirtiest data. Some matches were washed out, some ended under Duckworth-Lewis, and several never had a complete ball-by-ball log uploaded at all. Where there is no row, I do not enter a zero — I enter "unknown." That single decision changes the shape of the entire baseline. Now to the cricket itself, because the real fold in home advantage hides inside the overs, not in the result. Across all my coded matches, the home side's run rate in the powerplay (overs 1–6) was 8.1, the visitor's 7.8. A gap of only 0.3 — so small it is not an advantage, it is noise. But in the middle phase, overs 7 to 15, the home side scored at 7.4 while the visitor managed 7.0. Again the gap is small, but the picture changes: in this phase the home side's dot-ball rate is nearly two percentage points lower than the visitor's, and its scoring rate against spin is higher. Home advantage, in other words, is not built on a storm of boundaries but on the patience of avoiding them. The real difference arrives in the death overs, 16 to 20. Here the home side's strike rate is 142, the visitor's 129. That thirteen-point gap is what interests me most, because it is not an event, it is a process. Who is batting, who is bowling, and who is absorbing pressure — the sum of those three variables produces the thirteen points. I reopened the ball-by-ball log and found that in the death overs the home side received a higher share of yorkers and slower balls than the visitor. Chasing the reason, I found something the scoreboard never shows: when the ball changes hands in the death overs, the fielding side's field placement is already set for the home batter, because the coaching staff know how the pitch behaves. This is not match-fixing; it is a preparation advantage, and preparation advantages are written down nowhere. This brings me to my second note: the toss. In roughly 62 percent of my coded floodlit matches, the toss-winning side chose to field. The reason is obvious — dew. But the interesting part is that dew does not affect both sides equally. The side batting before dew falls generally gets a dry, quick pitch; the side batting later gets a slow, damp one. In my log this difference is almost absent in daylight matches and obvious under floodlights. A large part of home advantage is, in fact, the timetable of dew — and the timetable is set by the ground administration. This is where I reconcile the columns by hand before calling anything a trend, because a danger is hiding here: correlation is not causation. The easy story is that crowds returned, so home advantage returned. My data supports that story but does not prove it. Three other things changed at the same time the stadiums emptied. First, the schedule was compressed, so teams played back-to-back matches with shorter rest gaps. Second, travel restrictions meant visiting sides often stayed at the same venue continuously — the very meaning of "tour" changed that season. Third, pitch curation changed too; my notebook records that many matches used pitches built for spinners, to finish games quickly and reduce infection risk. If I do not separate those three variables, then the sentence "crowds mean home advantage" is an incomplete equation. To me home advantage is probably the sum of three layers: crowd pressure, pitch familiarity, and the dew timetable. Which contributes how much, I cannot say without another season of data — and without it I will not call the number final. And here the question of silent rows arises, the most neglected part of this whole calculation. One thing I want to state plainly about Bangladesh's cricket record-keeping: many matches, innings, and sessions exist whose ball-by-ball data was never fully preserved. These are not gaps, they are record-keeping failures. And their greatest cost is that they distort the baseline. If ten matches of a season have no data, and we declare "home advantage fell this season" based on the rest, we are effectively deciding from silence. I classify every gap — true zero, missing-at-random, and unobserved. A rain-abandoned match is not a "zero," it is "missing." A match with a scorecard but no ball-by-ball log is "partial." A match with no document at all is "unknown." Blend these three classes together and the average you get is not an average, it is an error. To me the fix is technological, and this is where I want to use the idea of blockchain — not merely as a metaphor, but as a principled demand. If cricket's scorebook were an immutable ledger, where every ball, every edit, every correction was written with its timestamp and version number, then today we could not stand over the columns and say "the numbers do not agree." We would know who changed what, and when. The real lesson of blockchain here is not secrecy but immutability — once written, it cannot be erased, only amended by a new entry, with the old entry preserved. Cricket's scorecard needs exactly this quality. Because in Bangladesh cricket there are still matches with two different scores written in two places, and no one can say which is real. I say this not out of fascination with technology but for a practical reason: if every match's data were written so that no one could later alter it silently, the analyst's job would not become easier — it would become honest. The ledger is patient, but the ledger is not amnesiac. Now back to the match I began with. Overs eleven to fifteen — for me the equivalent of minute sixty, the moment the match stopped obeying its earlier script. The home side won, but without that pattern of spin usage and outside-off lines across those five overs, the result could have flipped. The scoreboard turns those five overs into a short story; my log keeps them as a chain of decisions. The lesson I take is not a trend but a signal: in the BPL, home advantage lives in the economy of the death overs, not in the noise of the crowd. And that economy is governed by who is bowling, how prepared the field placement is, and when the dew falls. For the coming rounds my eye will be on three things. First, the rate of spin usage between overs 7 and 15 — where that rate suddenly rises, the tempo of the match turns. Second, the tendency to choose to field after winning the toss and its relationship with dew. Third, and most important, how completely each match's data is being preserved — because a season with empty rows is a season whose story we will never tell correctly. I know talking about incomplete data is uncomfortable, because readers want a final number. But fifty years of experience tells me that before calling a number final you must know its denominator — how many balls, how many matches, how many seasons, and how many rows are silent. And if the answer is "we do not know," then that is what must be written. Because the ledger never forgets, but we do — and that forgetting is the most dangerous thing of all, especially where the record is genuinely everyone's property. When the next ball is bowled, I will have the notebook open. Because what happens inside the over is the truth; the rest is commentary.