A dash is not a zero

When a panel cell shows —, that is not the same as showing 0. They are two different pieces of information, and confusing them changes how you read the match.

Zero is a measurement: someone watched the match and counted none. A dash is the absence of measurement: that statistic does not exist for that match.

Why it matters in practice

Picture two rows in the table, both with the shots-on-target column blank. In the first, the match is 40 minutes old and neither side has hit the target — that is a stifled match, and it is strong information. In the second, the competition simply does not send that data, and the match may have had six shots on target you will never see.

If both showed zero, you would read both as stifled matches. One of them would be wrong, and you would have no way to tell which.

The zero that becomes a dash

There is one case where the panel distrusts its own data. When the source sends a zero for a competition that is known not to cover that statistic, the panel turns that zero into a dash.

It is a deliberate choice, and it has a cost: now and then a true zero becomes a dash, and you lose real information. We prefer that error to the opposite — an invented zero looks like a measurement and cannot be checked.

What to do with a row full of dashes

Use the columns that have numbers, and do not treat absence as a low value. A match with half its columns blank is not a quiet match: it is a match you are seeing half of.

Which competitions arrive with which data, and why, is in why some competitions have fewer numbers.

And if you build your own highlight conditions, this is worth knowing: a blank cell never satisfies a condition. A filter for “shots on target above two” will not light up in a match without that data, even if the match has five.

Updated on Oct. 31, 2026.