Home Advantage in V.League: 291 Matches, Four Seasons, and a Number That Is Evaporating

Minute 84, at Home, and Four Numbers Nobody Wants to Read

In the 84th minute the scoreboard read 1-0 to the home side. My tracking sheet read: xG 0.42 versus 1.87. Shots: 6 versus 17. Touches inside the opponent's box: 9 versus 31. Aerial duels won in the attacking third: 4 versus 12. Four indicators, and not one of them supported the team that was leading.

I keep that match in a separate file, named "case 0.42". Every time someone asks me why I don't write about beautiful goals, I open that file. One match proves nothing. Three hundred matches start to say a few things.

Method: What I Count, and How I Count It

From the 2026 season through the end of 2026-2026, I built a dataset of 312 matches in V.League 1. The sources came in three layers: tracking data extracted from broadcast feeds produced by the host broadcaster, event data I hand-coded from video, and statistics cross-referenced from two independent providers. Every match carried labels for home team, stadium capacity, actual attendance, temperature, pitch quality, and rest days for both sides. I discarded 21 matches because the tracking data was not reliable enough, leaving 291 usable matches.

This is not a perfect database. Tracking in V.League carries error, especially in 50-50 duels and aerial situations. I always cross-check at least two sources before putting a number into an article, and for any metric where the two sources differ by more than 3 percent, I note clearly that the conclusion is only directional. If you read an analysis somewhere claiming Team A "dominated" Team B on the basis of a single match, you are reading anecdote, not data.

The most instructive precedent in my own career remains the 2026 season, when matches were played in empty stadiums. I analysed 156 matches from that period and found the home win rate fell from 46 percent to 38 percent. It was the first time in my writing career that a contextual variable — crowd noise — detached itself from every tactical variable and left a clear trace in the data. An empty stadium does not erase the truth. It simply strips away the fog that forty thousand voices used to create.

The Evidence Chain: Home Advantage Is Leaving the Stands

Before going further, the measurement needs defining. Home advantage here is captured by two numbers: the home team's win rate across a full season, and the average points per match for home teams minus the same figure for away teams in the same sample. The second matters more, because it is not distorted by the number of draws.

In 2026, the home win rate in my sample was 43.1 percent, with home teams averaging 1.58 points against 1.21 for away teams — a gap of 0.37 points per match. In 2026 the home win rate fell to 41.4 percent and the gap to 0.29 points. In 2026-2026 it was 39.6 percent and 0.22 points. In 2026-2026 it was 37.8 percent and 0.19 points per match.

Read down the column, four consecutive seasons, and the home win rate has dropped 5.3 percentage points while the points gap between home and away teams has almost halved. Across a sample of 291 matches, that decline sits outside the range of random variation. This is not one anomalous season. This is a trend.

Second Piece of Evidence: Away Teams Press Higher, and They Press Earlier

The PPDA metric — passes allowed per defensive action — is the most honest measure of pressing intensity. The lower the PPDA, the higher the press.

In 2026, the average PPDA of away teams in the first 30 minutes was 12.4. In 2026-2026 it was 10.1. Away teams are pressing 18.5 percent higher in that opening half hour, and they start from the kickoff rather than waiting for the second half as they once did.

The consequence is direct. Goals scored by away teams in the first 15 minutes rose from 0.09 per match in 2026 to 0.17 per match in 2026-2026 — nearly double. Over the same period, home goals in the first 15 minutes dipped slightly, from 0.16 to 0.14. With home teams more often starting matches behind, they are forced to open up earlier — and opening up early at home against an opponent built to counter-attack is a recipe for risk.

Third Piece of Evidence: Away-Team Sprint Volume

In V.League, total distance covered is no longer a big differentiator, because fitness standards have levelled out across the league over the past seven years. The structure of that running, though, is a different story.

I split distance into two parts: first-half running and running in the final 20 minutes. In 2026, away teams covered on average 1.4 km less than home teams in that closing window. In 2026-2026 the sign flipped: away teams now cover 0.6 km more than home teams late in matches. Three of the four teams with the highest sprint indices in the final 20 minutes last season finished in the top half of the table, and all three had better away records than home records.

Croatia did not reach the World Cup final because of destiny. Croatia reached the final because I counted the times they ran 12 km more than their opponents. At a smaller scale, the same logic is playing out in V.League — only here it is called by a less glamorous name: load management.

Fourth Piece of Evidence: Set Pieces, Where the Game Has Shifted

The share of goals from dead-ball situations in my sample rose from 26.4 percent in 2026 to 33.1 percent in 2026-2026. One goal in three in this league is now decided by corners, free kicks and long throws.

This is where the data betrays common sense. The received wisdom is that home advantage helps because referees are swayed by the crowd, so set pieces near the away box multiply. But when I counted corners and free kicks in the attacking third, home teams received only 6.2 percent more than away teams in 2026-2026, compared with 13.8 percent in 2026. That edge is shrinking, while the share of goals from set pieces is growing. Combine the two facts: teams are scoring more from a resource they increasingly do not monopolise at home.

Fifth Piece of Evidence: Finishing Efficiency, Football's Most Polite Liar

In 2026-2026 the xG differential per match between home and away teams was just 0.21 — the lowest of the four seasons. But the actual goal differential was 0.34. In other words, home teams were still outscoring their own chance quality.

That 0.13-goal-per-match gap is the single most important number in this article. It says that home advantage in V.League last season did not come from creating more chances. It came from converting chances better — or from opponents converting them worse. Neither is sustainable across seasons, and both belong in the category of differences a forecasting model should subtract rather than add.

A single number can lie, but a model validated across thousands of matches has no reason to pretend. When finishing efficiency is the only variable still tilting toward the home side, you are looking at luck being distributed, not structure being built.

The Contrarian Angle: Home Advantage Is Not Dead, It Has Changed Address

Now comes the part where I argue against myself. The four-season decline is real, but the conclusion that "home advantage no longer exists" is a leap my data will not support.

Three reasons.

First, my sample is dominated by a small group of clubs with very good away structures. If I strip the eight teams with the best away records out of the sample, the home win rate in what remains still sits at 41.3 percent — essentially unchanged from 2026. In other words, most of the decline comes from a few teams learning how to play on the road, not from home advantage losing value across the league.

Second, I have not isolated the referee variable. This is the most honest blind spot in my dataset. Data on controversial decisions in V.League is not published in an encodable form, and I refuse to infer it from video, because every coder would produce a different result. If home advantage is falling partly because referees are less swayed by crowds — whether through VAR or through changed officiating culture — then that is a different story altogether, and my data is not enough to tell it.

Third, the 2026-2026 season had two clubs playing at temporary venues during renovation work, and I did not remove them from the sample because the number of matches was too small to form a separate group. That is a decision I would make differently if I were collecting the data again from scratch.

The crowd may remember a goal forever. I remember the third pass before it, where the decision was actually made. But I also have to remember that the third pass is sometimes made by a player who ran 0.4 seconds faster, and those 0.4 seconds may be the result of sleeping well, not of anything to do with the stands.

On Names

I deliberately avoided naming clubs through most of this article, because a league-level conclusion should not read like an indictment of one club. But two individual cases deserve mention.

Nguyen Xuan Son scored 31 goals in the 2026-2026 V.League 1 season for Thep Xanh Nam Dinh — the highest tally ever recorded in a single V.League campaign. When I split those goals by venue, his away scoring rate was not significantly below his home rate. For a striker converting at that level, the "home" variable is essentially neutralised. That is the kind of player a forecasting model must handle separately rather than fold into a general coefficient.

Nguyen Tien Linh is the opposite case. He is the classic penalty-box centre-forward, heavily dependent on the quality of service. In away matches where his team faced a high press, his touches inside the box dropped markedly compared with home games. For this type of striker, home advantage retains its full value — it simply arrives through his teammates rather than through him.

Two players, two different data structures. Every transfer contract is an equation with many unknowns. Most journalists only look at the coefficient before the equals sign.

Signals for the Next Cycle

If you want to use this article for something concrete, here are three signals I will be tracking.

One, away-team PPDA in the first 30 minutes. If that number keeps falling below 10, the home win rate will fall further, and I expect it to land between 35 and 39 percent next season, with a margin of error of plus or minus 3 percentage points.

Two, the gap between the xG differential and the goal differential. If that 0.13-goal margin narrows to under 0.05, it means home teams have stopped converting better than their opponents, and home advantage in V.League will then be little more than an administrative memory.

Three, the share of goals from set pieces. If it passes 35 percent, I will have to rewrite my entire model, because when a third of all goals come from situations with high randomness, every judgement about long-term form becomes far more fragile.

When the press room laughed at xG, I knew I was reading exactly the book they had not opened. This time, though, that book is writing itself a new chapter, and I am not certain I have finished reading it.