From Mirpur to Melbourne: A Seven-Variable Audit of Home Advantage in Asian Cricket and the On-Chain Truth of the Scorecard

I opened my notebook in the third row of the Mirpur Sher-e-Bangla press box exactly as the scoreboard glowed at 87/2 after fourteen overs. More than half the seats were empty—one evening in the first full series after the pandemic. Dew was settling on the pitch, the ball was keeping low, and the spinners' economy was dropping below four an over. In my live thread I wrote a single line at that moment: today's match is not written in the grass, it is written in the humidity of the air. When I reconciled the scorecard at the end of the night, I found a number that shook a belief I had held for seven years. The spreadsheet remembers what the stadium forgets—and that night the stadium forgot that its seats were empty. I began with the live thread and ended with a broadcast truth. About seven years earlier, in Sydney, I built an xG model for an A-League Grand Final. The match finished 1-1, decided 4-2 on penalties. But my model gave the home team 1.8 xG against the away team's 0.9, with a home PPDA of 9.8. The scoreline was telling one story; the model was telling a different one. That night an instinct lodged itself in me: the truth standing behind any result is not on top of the scoreboard, it is buried underneath it. Returning to cricket, I apply the same principle—I no longer explain home advantage through any single cause. Before discussing home advantage in Asian cricket, three things need clearing up. First, Asia's pitches and weather are not one thing—Mirpur in Dhaka, Zahur Ahmed Chowdhury in Chattogram, R. Premadasa in Colombo, Chepauk in Chennai and the Melbourne Cricket Ground cannot be measured on the same scale. Second, the crowd and the humidity of the air are two separate variables, yet broadcasts routinely merge them into one. Third, during the regular season the weights of these variables shift match by match, and that shifting is the real story. From my tracking data I have separated seven variables, drawn a separate coefficient for each, and identified which one genuinely flips a result and which is merely received belief. First, the source and limits of the data. This notebook is built from over 200 matches across Asian domestic and international cricket, recorded ball by ball. For every match I have logged attendance, pitch type, first- and second-innings run rates, powerplay wides, dot-ball percentage, the toss result, the time dew arrived, and how much lower the ball came through in the second innings. My model's coefficients are estimates and sample-dependent—I do not claim them as final truth, but present them as a preliminary design to be tested in the next series. Only after cross-checking against video and ball-tracking do I call a number a broadcast truth. I do not trust the eye test until the data signs the same sheet. Now the seven variables. I will set out my model's estimated weights in a table first, then explain each. Variable | Estimated weight | Sample | Signal Pitch behaviour | 27% | 112 matches | On spin-friendly pitches the home team's spin economy is on average 0.8 lower Dew and humidity | 22% | 74 matches | Second-innings chase success rises by 21% Crowd and noise | 18% | 68 matches | With a full crowd the home team's run rate rises by 0.35 Travel and recovery | 13% | 54 matches | The visitor makes on average two extra fielding errors in the first match Toss | 11% | 90 matches | In dew-prone matches the toss-winning team wins 64% Umpiring | 5% | 40 matches | Boundary decisions favouring the home team average +1.2 Scorecard integrity | 4% | 28 matches | In unverified data the correction rate exceeds 3% Pitch behaviour carries the largest weight in my model, and it is the most misunderstood. A typical turning track at Mirpur—where the ball turns about four degrees by the fifth day—is a measurable weapon for home spinners. In my sample of 112 matches, on spin-friendly pitches the home team's spin economy was on average 0.8 runs lower, and that gap is larger than the crowd effect. But here lies a trap: that 0.8 is not exclusively the home team's—if the visitor adapts to their own spinners, the gap falls by roughly half. The pitch is a context coefficient, not a team's permanent property. On Melbourne's bouncy pitch the same spin-economy advantage is near zero, proving that a context coefficient travels but does not colonise. Dew carries the second-largest weight in my model, and it is the most treacherous variable in Asia's evening cricket. In a sample of 74 matches I found that once dew begins, second-innings chase success rises by 21%. The reason is plain—the ball becomes slippery in the hand, spinners lose grip, and boundaries come easier. This number is directly tied to the toss, which I explain below. But dew is a time-dependent variable—as much as it matters in the 20th over, it does not in the 40th. So I treat dew in two bands: light dew (effect roughly negligible) and heavy dew (effect decisive). Broadcasts routinely erase that distinction. Crowd and noise—this is the variable most heavily romanticised, and it is exactly where my data teaches the most humility. With a full crowd the home team's run rate rose by an average of 0.35, real but not exaggerated. After stadiums emptied in 2026, I analysed 24 matches and found home teams' expected score fell from 1.45 to 1.12. Returning to cricket I saw the same softening—but the interesting part is that even in empty stadiums home advantage did not vanish. The crowd is a variable, but it is not a single cause. Empty seats taught me that home advantage is a variable, not a myth. Travel and recovery—this variable does not appear on the table, yet it shapes series results deeply. In a sample of 54 matches, the visiting team made on average two extra fielding errors in the first match of a series. The cause is simple—jet lag, sleep cycles, and unfamiliar outfields. Even the two-hour road trip from Dhaka to Chattogram is a travel variable that television never shows. During the regular season it affects selection and bowling rotation, especially in back-to-back matches. The toss—in Asian cricket this variable is often exaggerated, but in dew-prone matches it is decisive. In a sample of 90 matches, in dew-prone games the toss-winning team won 64%. That sounds like toss-driven luck, but it is really a structural problem—the interaction of pitch and weather raises the value of the toss. In my model the toss weight is only 11%, because it activates only on dew-prone evenings. On dry days its effect is near zero. So I treat the toss as a conditional variable, not a permanent edge. Umpiring—discussing this variable requires caution, because here the numbers are small and the noise is loud. In a sample of 40 matches I found boundary decisions favouring the home team by an average of 1.2. This is not proof, but a signal—one that needs a larger sample and independent verification. I am not making an accusation; I am only recording that this variable carries the greatest uncertainty and should not be used in conclusions. Scorecard integrity—this seventh variable is the newest and most important to me. My entire analysis rests on one assumption: that the scorecard I am reading is correct. Yet in a sample of 28 matches I found that in over 3% of cases the ball-by-ball record needed correction—wrong runs, wrong ball counts, wrong strike rates. That 3% sounds small, but a tournament's points table and net run rate hang on it. This is where I turn to blockchain. I do not see blockchain as a crypto market, but as a ledger of integrity. Cricket data's greatest weakness is the centralised scorebook—a single wrong entry spreads everywhere, and when it is later corrected, no one sees it. An on-chain scorecard—where every ball is an immutable entry—can offer a structural fix. If every over is recorded with a timestamp, no one has to hunt for proof when a result is later questioned. I timestamp every hypothesis in my live thread for exactly this reason—so that when broadcast data arrives, I know what I thought and when. An on-chain scorecard is that same discipline, applied to the whole game. Seen together, these seven variables reveal an interaction that no single variable shows. Example: dry pitch + empty crowd + light dew—in that combination home advantage is near zero. But spin-friendly pitch + full crowd + heavy dew—in that combination home advantage exceeds 25%. Home advantage is not a constant; it is a combination. In 2026, at the Euros and the Tokyo Olympics, I saw a similar interaction in football—Italy's high press (PPDA 10.8) against Canada's low block (only 0.7 xG per match). Two different strategies, yet measurable in one framework. In cricket I use the same method—a portable framework that measures across formats and nations, but whose coefficients change when conditions change. Now the contrarian angle. The common claim is that the crowd is the main engine of home advantage. My data partly disagrees. The crowd's weight is 18%, while pitch and dew together are 49%. In other words, the two inanimate variables of weather and pitch carry roughly three times the effect of the crowd. Broadcasts avoid these two inanimate variables, because a roaring crowd is easy to show and dew is hard to measure. Here lies the difference between correlation and causation. There is a relationship between crowd and home victory, but the cause is partly hidden in pitch selection and scheduling. The home team knows which pitch it will play on and when—a structural edge, not merely emotion. A second contrarian observation: in my sample of 68 matches, while the home team's run rate rose with a full crowd, its wicket-loss rate also rose somewhat. The crowd does not only embolden; it also pressures. An aggressive shot that would not be attempted in an empty stadium is attempted before a full house—and sometimes it costs. The spreadsheet captures this subtle duality; the stadium does not. I add a caveat here. My coefficients come from over 200 matches, but they are one researcher's tracking—not a large broadcast dataset. So I call every number provisional, not final. I pre-registered my variables before matching them to the data, so that the temptation to fit numbers to a story would not arise. I treat the context coefficient not as an off-the-shelf solution but as a careful adjustment whose limits must be admitted. This whole analysis has one practical lesson. In the coming series, when you see a team win the toss and choose to field, think—this is not courage, it is a dew-model calculation. When you see a spinner concede only eighteen runs in six overs at Mirpur, understand—this is not merely skill, it is the match between his skill and the pitch coefficient. When you see a visiting side drop two catches in the first match, know—this is not merely a lapse in focus, it is the arithmetic of jet lag. What the broadcast calls emotion, the spreadsheet calls a variable. I began with an empty stadium and I end with the possibility of an on-chain scorecard. A number is a witness; a trend is a confession. A 3% correction rate may sound small, but the fate of a tournament semi-final hangs on that small number. If, next season, an Asian board records its ball-by-ball data on-chain, I will begin my first analysis from there—because then my variables will rest not on assumption but on proof. The match ends, but the model keeps playing. In the next series, watch the toss decision, the timing of the dew, and the visitor's first-match fielding—the truths the scoreboard never tells you are hiding exactly there.