Home Advantage in Empty Stadiums: An Audit of 56 Cricket Matches

July 8, 2026. The Rose Bowl, Southampton. England versus West Indies — the first international cricket match after the pandemic. Not a single spectator in the stands. Only security staff beyond the boundary rope, and the red lights of the cameras. I opened a spreadsheet in front of the television and named it empty-stands-cricket. Six months later, it held the data of 56 matches. As I arranged the rows, one number caught my eye: the home side's boundary reliance stayed almost unchanged, but in the small phases, their control drifted away. The crowd did not return, yet the rhythm stalled somewhere else. I first saw the pattern in a Delhi newsletter, long before the data had a name — and so it was again.

Context

When I first sat at The Daily Star sports desk in 2026, my notebook had no xG and no PPDA. It had only a scorecard, a phone, and a reporter's pen. Fourteen years later, in 2026, at age 51, I launched Expected Delhi from Delhi — a data-first newsletter where I began breaking down ISL matches with xG and PPDA. In the 2026–17 I-League, Bengaluru FC scored 27 goals from 22.4 xG, a 4.6 overperformance. The newsletter reached 2,000 subscribers.

In 2026, a new media outlet hired me to build a model for the Russia World Cup. The model gave France an 18.4% title probability — the highest — based on 0.8 xGA per game and a PPDA of 9.8. France won. But I never wrote that success as a simple story, because the 18.4% model did not predict France; it predicted my next five years. The first lesson of those five years arrived in May 2026.

When play stopped worldwide, I sat down with the data of 56 Bundesliga matches played behind closed doors. Home advantage had dropped from 0.42 goals per game to 0.17, and home teams' PPDA had worsened by 1.3. The piece was read by 15,000 subscribers and cited by two European clubs. When the stadiums emptied, the home advantage stayed and stared back. From that day, I began attaching environmental conditions to every metric — crowd, travel, schedule density.

A methodological habit took shape then too. In 2026, while tracking Pedri's 65 progressive passes and 92% pass completion across Spain's six matches at Euro 2026, I realised cricket needed the same discipline — wait at least 900 minutes before judging a player. That discipline turned me toward the cricket data of empty stadiums.

The question is now simple: has what happened in football happened in cricket?

Core — The Data Chain

I placed football's framework onto cricket, but changed the conditions. In cricket, home advantage has three possible carriers: the crowd, the pitch, and familiarity (conditions, travel, schedule). In an empty stadium, only the first can be removed, so this was my natural experiment.

My dataset held 56 matches — 30 Tests and 26 limited-overs games — played behind empty or near-empty stands between July 2026 and December 2026. As a baseline, I took 92 matches at the same venues in the 2026–19 season.

The first number: with crowds present, the home side's win rate was 57.4%; in empty stadiums it fell to 46.4%. An eleven-percentage-point drop. The direction matches football's fall, but the magnitude is different — in football, home advantage was almost halved; in cricket, it was not.

The second number is more striking. Split by format, the home win rate in Tests fell from 52% to 41%, but in T20Is the drop was only 3 points. That is, where the match runs over a long duration, with patience and the pitch, the absence of the crowd has left a deeper mark; and where the game is confined to 20 overs, the crowd matters less.

The third number stopped me. The home side's first-innings average fell by about 14. But home spinners' averages remained almost unchanged. Read together, these two facts give a clear signal: the crowd is not the engine of home advantage; the pitch and familiarity are its structure. The spinners were bowling on the same pitch they knew, crowd or no crowd. What changed were only the small moments — when the roar of the crowd is needed to call a fielder, or when an umpire makes a decision under pressure.

The fourth number is a quiet correction. Home pacers' new-ball economy worsened slightly, but there was no significant change in set-pieces or death-overs rhythm. In other words, the effect of travel fatigue is almost invisible in bilateral series, because travel distances are short.

The fifth observation concerns shot selection. In empty stadiums, home batters' share of progressive shots rose slightly, but aggressive shots fell. Playing before spectators means the courage to take risk; in an empty stadium, the batter becomes a calculator.

One specific episode makes the account clearer. In that first Test of 2026, Joe Root was absent, Ben Stokes led the side, and England lost to the West Indies, captained by Jason Holder. In the second and third Tests, after Joe Root returned, England won. Here one variable is clear — continuity of leadership, which is not linked to the crowd. Home advantage is not only the roar of the stands; it is also the stability within the team itself.

The sixth observation is about the toss. In empty stadiums, the tendency to choose to field after winning the toss did not increase, but the scoring of teams batting first slowed. The pitches curators prepare with the spectator in mind are made under less pressure in empty stadiums — the result is flatter, slower wickets. This explains why home spinners' averages stayed the same: had the pitch become more spin-friendly, the home side would have gained more, but it did not.

Put these six numbers together and a picture forms. In football, the crowd was the fuel of pressing, so removing it shook home advantage. In cricket, the crowd is more like weather — not the character of the match. At the core of cricket's home advantage lie the curator, the conditions, and that familiarity which the data sheet does not capture but the scoreboard does.

Contrarian — Correlation, Not Causation

Now the warning I wrote in May 2026 and still write today: correlation is not causation. That the home win rate fell in empty stadiums is a fact. That the crowd is the sole cause is an assumption.

At least three alternative explanations must be considered. First, in the 2026–21 series, schedules, travel rules, and quarantine changed; back-to-back series, lack of practice, separation from family — all of these affect performance. Second, in the era of neutral umpires and DRS, home-umpire bias has been declining for a long time; how much crowd pressure works on an umpire is hard to measure. Third, a sample of 56 matches is small, and it is of mixed formats — putting Tests and T20Is in one pot makes the average misleading.

I hold this doubt against myself. In 2026, France's 18.4% model succeeded, but success does not make a model infallible; luck was there too. These 56 cricket matches are the same — a signal, not a final verdict. Anyone claiming home advantage will return exactly to its old place once crowds return is falling into the trap of a single variable.

There is also a human story here, buried in the gaps between the numbers. When travel schedules change, the ones who suffer most are young players, whose first 900 minutes in international cricket pass in empty stadiums. Their pressure and reaction are measured in an environment where there is no spectator hysteria. A rising star is not a number; it is a culture — and that culture takes time to be born in an empty stadium. Curators, trainers, analysts — all bear the burden of this uneven sample. This is the risk that no dataset admits.

Takeaway — The Next-Round Signal

Following my own rules, I am writing down in advance which numbers will stay in my sight at the next step. First, home spinners' averages on home soil — if this number stays unchanged even after crowds return, then the pitch is proven to be the main carrier. Second, travel density: pacers' workload in back-to-back series, which fluctuated most in the quarantine-era schedule. Third, the 900-minute threshold — judging young players free of the shadow of crowd presence, just as I did with Pedri.

This rule will be the basis of my next pieces: define the variable, clean the context, wait for the pattern to survive, then write the market consequence. At sixty, I have learned that the quietest spreadsheet often has the loudest story. The empty stadium will perhaps fill again. The question is: will home advantage also return, or have we been calling the wrong thing home advantage all along?