The Video Assistant · Lesson 4 — The Right Calls ← Course

The right calls — which examples to show.

Which examples you pick matters more than how many. Two sets of the same size can behave completely differently.

Last page was about how many. This one is about which — and it matters more. The model learns the pattern from whatever you put in front of it, so the examples you choose quietly decide what it thinks the job is.

Show it three easy, obvious handballs and it learns an easy, obvious rule — “ball hits arm, give a penalty.” Fine, until a tricky one comes along: the ball flicking an arm that is tucked right against the body. Was that a handball, or did it strike the body first? The model that only saw arms flung out will give the penalty, and be wrong.

The fix is to choose examples that mark the line between penalty and no penalty: include a case that went the other way (a ball off the body given no penalty) and a genuinely close one. Now the model has seen where the line sits, not just the easy middle — and it reads the tricky incident correctly.

Same number of examples either way. The difference is entirely in the choosing.

Show the VAR each set of three, then watch how it rules the same tricky incident. Only one set has seen the close calls.

Two sets, three examples each. Pick one and see how it rules the tricky incident.
examples shown3 examples
↓ then the tricky one ↓
the tricky incident · arm near the body
the VAR's call

Same count, different examples.

Both prompts hold three examples. Swapping which three — not how many — is what flips the tricky call from wrong to right.


      

It's which, not how many.

Three easy examples teach an easy rule, and the model breaks on the first tricky case. Three well-chosen examples — one that crosses the line, one that sits right on it — teach the model where the line falls, and it holds up. Same count, very different behaviour.

Choose examples that mark the close calls, not just the obvious middle. A contrasting pair — one each side of the line — is worth more than a pile of look-alikes.

Next: bad calls →
Go deeper — choosing examples well optional

Cover the close calls, not the centre

A model generalises from what it is shown. If every example is a clear-cut case, it never learns where “clear-cut” ends. Deliberately include cases near the line — the ones a person would have to think about — because those are the ones it will get wrong otherwise.

Contrast teaches the rule

A pair that goes opposite ways on a small difference (handball vs ball off the body) teaches far more than two examples with the same verdict. The contrast is the lesson: it shows the model which detail actually decides the call.

Diversity beats volume

This is why the last page's rule holds: a few varied, borderline examples beat many similar ones. When you are tempted to add a fourth example, ask whether it shows the model something new — or just repeats a case it has already seen.

Test on the cases you fear

Pick your examples, then try the prompt on the awkward inputs, not the easy ones. Where it fails tells you which example is missing. This is the same instinct the course returns to much later, when it asks how you'd measure whether a VAR is any good at all.