December 18, 2025 • 9 mins
Drama and a technical issue in the Ashes yesterday, with Alex Carey scoring a welcomed century as Australia ended the day at 326-8, but he should've been out when he was on 72.
England used a review on a caught behind as they thought they heard a sound - the microphones on the stumps showed a clear noise but was shown on the replay as happening before the ball passed the bat resulting in a not out call.
Afterwards, Carey admitted that he had hit the ball and the company that owns Snicko concluded that the operator at the time must have chosen the incorrect stump mic for audio processing.
Sir Ian Taylor is known for his work with televised sport - and he joined D'Arcy to discuss.
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Episode Transcript
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Speaker 1 (00:06):
You're listening to the Sports Talk podcast with Dancy Wildergrave
from News Talk Zed.
Speaker 2 (00:11):
Be right, let's talk about the ashes from yesterday. A
bit of drama, a technical issue. Alix Carey scored a
century apparently should have been out when it's in seventy two.
Speaker 3 (00:21):
They used to review the.
Speaker 2 (00:22):
English on a court behind. They thought they heard something
microphones on the stumps, clear noise, but the replay reckon
it all happened before past the back, resulting in a
not out call. So Kerry comes out, admitted that he
hit the ball, and the company that owns Snacko concluded
that the operator at the time must have chosen the incorrect.
Speaker 3 (00:39):
Stump MIC for audio. How do you do that?
Speaker 2 (00:42):
There must be a process, not this one, this one.
Sirien Taylor joins us now he's a synonymous with the
on screen graphics for televised sporty runs Virtuly and duaneed
and Ian thanks very much for joining us. Of course
your name sprang to mind straight away after the Snacko
issues and the ashes yesterday, and you're at the forefront
of the way this technology works. So where are we
(01:03):
at at the moment that tech and sport identifying things
like next because this goes through all sport, right.
Speaker 3 (01:10):
Yes it does.
Speaker 4 (01:11):
And I mean the interesting thing is it's the technology
has been getting better. So we've been doing it for
I don't know many, twenty twenty five years, I guess,
and you know, just probably in the last three to
four years, we've noticed a massive move in the technology
that helps. And of course everyone talks about AI, but
(01:34):
AI and machine learning is another thing that is really
make a big difference. I think where they got caught
out yesterday. We don't do the edge detection.
Speaker 2 (01:45):
We do the.
Speaker 4 (01:46):
Runouts and the LBWs, but I think they got caught
out yesterday, and the edge detection is it's really important.
Speaker 3 (01:53):
You've got two elements.
Speaker 4 (01:54):
You've got what the camera is seeing and what the
microphone's hearing, and those have to be absolutely aligned with
each other, or you get the thing that appears to
have happened yesterday. And from the reports I'm seeing, those
two were out of sync. And so that puts the
umpires in a bit of a tough position because they
can see the ball is past the bat when they
(02:17):
see the sound, So what do they do? You know,
They've got to go with what they can see and
I think that was the issue that happened yesterday.
Speaker 2 (02:27):
You used to have hot spot, whatever happened to that.
That was a very popular piece of technology. What did
that get phase here?
Speaker 4 (02:34):
Yeah, so I thought hot thought was really really good.
It was only ever used in Australia. It didn't get
it picked up by the ICC.
Speaker 3 (02:43):
Well.
Speaker 4 (02:44):
One of the really important run of the ridicol things
about hotspot was that if you saw the edge of
the bat light up, it meant the ball hit the bat.
The issue two issues was that because well the same issue,
because the cameras were really really expensive. They're very specialist
cameras to be able to see that hotspot come up.
(03:07):
They couldn't afford to have as many as you'd need
to be able to see all the edges of the bat,
and so it just kind of priced itself out because
to be really effective you needed it on both sides
of the wicket, So that was at least four of
them that you needed, and probably a couple of front
on and it just got too expensive to use. But
(03:30):
you know what we're seeing with some of the new
technologies which will probably start you know, you'll start seeing
unravel in the next year or so, is that AI
and machine learning is helping to give way more certainty.
It's made a big difference with the lbw's not many
people question those anymore because the technology has advanced so
(03:51):
far with the high speed cameras well.
Speaker 2 (03:53):
It makes sense right that we do utilize AI and
its best form as opposed to the scary, more sinister
sides of it. So this is good, But does that
make the technology after it's being developed? Does that make
it cheaper or less expensive? I will it be accessible
right around the globe? Because in order for it to
be I suppose given the tick by the ICC, it
(04:15):
needs to be applied everywhere, doesn't it. It can't just
be with the Aussies or you know, with a couple
of high rolling teams. So is it going to be
many cheaper really less expensive.
Speaker 3 (04:25):
No, it's it's getting less expensive.
Speaker 4 (04:28):
And the thing is that you know, there's there's two
edge detection applications now, so the one they are using
in Australia which is Snicko, and the other one is
called Ultra Edge. So you have to have ball tracking,
LBW and edge detection in every game now everywhere, and
(04:50):
so that is going all over the world, and I
think any changes that technology bring will be applied to everything.
One of the things we're seeing which is really interesting
is like I'm on my iPhone seventeen whatever it's called,
you know, whatever numbers they're up to at the moment,
and these things are getting incredibly sophisticated as well now,
(05:13):
so you know, they have lighter in them, they have
all sorts of things. So we're seeing that technology change
really fast, and it wouldn't surprise me if we start
to see iPhones being used in those lesser matches, maybe
even club matches in the future, to give people ball
tracking and maybe even you know, edge detection.
Speaker 2 (05:35):
The acceleration of this technology, you see, you've been around
twenty five years or so doing this exponential it's just
going through the roof. It's got how much more can
we do? You mean, you mentioned the seventeen, the iPhone seventeen,
But where's the limit? Is there a limit from what
you can see?
Speaker 4 (05:50):
Well, you know, it's funny you should say that because
you know, we started we started the company in nineteen
ninety and that was the same year that the World
Wide Web was invented, and it's sort of like, what's
this thing, the Internet? What are we going to use
that for? And the acceleration. In nineteen ninety two, the
(06:11):
computer we use to do the first America's Cup cost
half a million dollars and it was the size of
a fridge. And now you know, we deliver all this
stuff on the phone you carry in your pocket, so
that the escalation has been I guess over thirty five
years gradual. But the biggest change we've seen really is
(06:32):
AI and that has just gone to another level where
we're seeing the changes almost every week.
Speaker 2 (06:39):
As far as the work behind it, I'm presuming you've
got to input piles and piles of data, so much
data to I suppose train these machines up, it takes
the longest Doore.
Speaker 4 (06:52):
Yeah, that's a really good point because the thing about
about AI is it's only as good as you know
what it's been taught and how much it's been taught.
Speaker 3 (07:04):
So we have for the last year we have we.
Speaker 4 (07:08):
Built an AI engine for cricket, for scoring, for identifying,
you know, what are all the cool things happening in
a match. And over the last year we've put ten
thousand hours off cricket through it, and we've had students
from the Polytech and we've they come in and as
part of a training course, they sit in there and
(07:29):
they identify. So give you one example, and there's lots
of examples. Let's say the noball. So nobles get missed
quite often. You know, they look at them really closely
when they're when there's an appeal, but actually they get
missed a lot. So over the year there's been ten
thousand hours of cricket gone through our AI engine and
(07:51):
somebody has sat there and taught the AI exactly what
a noball is, and they we've got doing it now.
And the people who worked on it were laughing saying,
you know, it's taken us a year to teach AI
what a noble looks like, and all we have to
do is look at it and we know straight away.
Speaker 3 (08:12):
That's one side of it.
Speaker 4 (08:13):
But the other side of it is, now that the
AI knows what a no ball is, it can do
a million games at once, a million balls at the
same time anywhere in the world. And that's a huge
change that's happening now. So things get easier, they get cheaper,
they'll get done in the cloud, but it means you
can do it way more effectively because you've finally taught
(08:35):
it how.
Speaker 3 (08:36):
To do it cold come, but a lot of work.
Speaker 4 (08:38):
Yeah, oh, I mean it was probably the worst worst
person that could happen to, reallyy.
Speaker 2 (08:46):
But on that look, ten thousand hours. That's so significant,
Sirian Tartar, and thanks very much for joining us, because
you know, to become an expert in something, you've got
to spend ten thousand hours, right, that's our humans operator
as well.
Speaker 3 (08:57):
Mate, So well done it is, isn't it. Well you
have a great Christmas.
Speaker 2 (09:00):
Doting and you as well.
Speaker 3 (09:01):
Thanks for your time, yere goy dear see it.
Speaker 1 (09:04):
For more from sports Talk, listen live toin News Talks.
It'd be from seven pm weekdays, or follow the podcast
on iHeartRadio.
You're listening to the Sports Talk podcast with Dancy Wildergrave
from News Talk Zed.
Speaker 2 (00:11):
Be right, let's talk about the ashes from yesterday. A
bit of drama, a technical issue. Alix Carey scored a
century apparently should have been out when it's in seventy two.
Speaker 3 (00:21):
They used to review the.
Speaker 2 (00:22):
English on a court behind. They thought they heard something
microphones on the stumps, clear noise, but the replay reckon
it all happened before past the back, resulting in a
not out call. So Kerry comes out, admitted that he
hit the ball, and the company that owns Snacko concluded
that the operator at the time must have chosen the incorrect.
Speaker 3 (00:39):
Stump MIC for audio. How do you do that?
Speaker 2 (00:42):
There must be a process, not this one, this one.
Sirien Taylor joins us now he's a synonymous with the
on screen graphics for televised sporty runs Virtuly and duaneed
and Ian thanks very much for joining us. Of course
your name sprang to mind straight away after the Snacko
issues and the ashes yesterday, and you're at the forefront
of the way this technology works. So where are we
(01:03):
at at the moment that tech and sport identifying things
like next because this goes through all sport, right.
Speaker 3 (01:10):
Yes it does.
Speaker 4 (01:11):
And I mean the interesting thing is it's the technology
has been getting better. So we've been doing it for
I don't know many, twenty twenty five years, I guess,
and you know, just probably in the last three to
four years, we've noticed a massive move in the technology
that helps. And of course everyone talks about AI, but
(01:34):
AI and machine learning is another thing that is really
make a big difference. I think where they got caught
out yesterday. We don't do the edge detection.
Speaker 2 (01:45):
We do the.
Speaker 4 (01:46):
Runouts and the LBWs, but I think they got caught
out yesterday, and the edge detection is it's really important.
Speaker 3 (01:53):
You've got two elements.
Speaker 4 (01:54):
You've got what the camera is seeing and what the
microphone's hearing, and those have to be absolutely aligned with
each other, or you get the thing that appears to
have happened yesterday. And from the reports I'm seeing, those
two were out of sync. And so that puts the
umpires in a bit of a tough position because they
can see the ball is past the bat when they
(02:17):
see the sound, So what do they do? You know,
They've got to go with what they can see and
I think that was the issue that happened yesterday.
Speaker 2 (02:27):
You used to have hot spot, whatever happened to that.
That was a very popular piece of technology. What did
that get phase here?
Speaker 4 (02:34):
Yeah, so I thought hot thought was really really good.
It was only ever used in Australia. It didn't get
it picked up by the ICC.
Speaker 3 (02:43):
Well.
Speaker 4 (02:44):
One of the really important run of the ridicol things
about hotspot was that if you saw the edge of
the bat light up, it meant the ball hit the bat.
The issue two issues was that because well the same issue,
because the cameras were really really expensive. They're very specialist
cameras to be able to see that hotspot come up.
(03:07):
They couldn't afford to have as many as you'd need
to be able to see all the edges of the bat,
and so it just kind of priced itself out because
to be really effective you needed it on both sides
of the wicket, So that was at least four of
them that you needed, and probably a couple of front
on and it just got too expensive to use. But
(03:30):
you know what we're seeing with some of the new
technologies which will probably start you know, you'll start seeing
unravel in the next year or so, is that AI
and machine learning is helping to give way more certainty.
It's made a big difference with the lbw's not many
people question those anymore because the technology has advanced so
(03:51):
far with the high speed cameras well.
Speaker 2 (03:53):
It makes sense right that we do utilize AI and
its best form as opposed to the scary, more sinister
sides of it. So this is good, But does that
make the technology after it's being developed? Does that make
it cheaper or less expensive? I will it be accessible
right around the globe? Because in order for it to
be I suppose given the tick by the ICC, it
(04:15):
needs to be applied everywhere, doesn't it. It can't just
be with the Aussies or you know, with a couple
of high rolling teams. So is it going to be
many cheaper really less expensive.
Speaker 3 (04:25):
No, it's it's getting less expensive.
Speaker 4 (04:28):
And the thing is that you know, there's there's two
edge detection applications now, so the one they are using
in Australia which is Snicko, and the other one is
called Ultra Edge. So you have to have ball tracking,
LBW and edge detection in every game now everywhere, and
(04:50):
so that is going all over the world, and I
think any changes that technology bring will be applied to everything.
One of the things we're seeing which is really interesting
is like I'm on my iPhone seventeen whatever it's called,
you know, whatever numbers they're up to at the moment,
and these things are getting incredibly sophisticated as well now,
(05:13):
so you know, they have lighter in them, they have
all sorts of things. So we're seeing that technology change
really fast, and it wouldn't surprise me if we start
to see iPhones being used in those lesser matches, maybe
even club matches in the future, to give people ball
tracking and maybe even you know, edge detection.
Speaker 2 (05:35):
The acceleration of this technology, you see, you've been around
twenty five years or so doing this exponential it's just
going through the roof. It's got how much more can
we do? You mean, you mentioned the seventeen, the iPhone seventeen,
But where's the limit? Is there a limit from what
you can see?
Speaker 4 (05:50):
Well, you know, it's funny you should say that because
you know, we started we started the company in nineteen
ninety and that was the same year that the World
Wide Web was invented, and it's sort of like, what's
this thing, the Internet? What are we going to use
that for? And the acceleration. In nineteen ninety two, the
(06:11):
computer we use to do the first America's Cup cost
half a million dollars and it was the size of
a fridge. And now you know, we deliver all this
stuff on the phone you carry in your pocket, so
that the escalation has been I guess over thirty five
years gradual. But the biggest change we've seen really is
(06:32):
AI and that has just gone to another level where
we're seeing the changes almost every week.
Speaker 2 (06:39):
As far as the work behind it, I'm presuming you've
got to input piles and piles of data, so much
data to I suppose train these machines up, it takes
the longest Doore.
Speaker 4 (06:52):
Yeah, that's a really good point because the thing about
about AI is it's only as good as you know
what it's been taught and how much it's been taught.
Speaker 3 (07:04):
So we have for the last year we have we.
Speaker 4 (07:08):
Built an AI engine for cricket, for scoring, for identifying,
you know, what are all the cool things happening in
a match. And over the last year we've put ten
thousand hours off cricket through it, and we've had students
from the Polytech and we've they come in and as
part of a training course, they sit in there and
(07:29):
they identify. So give you one example, and there's lots
of examples. Let's say the noball. So nobles get missed
quite often. You know, they look at them really closely
when they're when there's an appeal, but actually they get
missed a lot. So over the year there's been ten
thousand hours of cricket gone through our AI engine and
(07:51):
somebody has sat there and taught the AI exactly what
a noball is, and they we've got doing it now.
And the people who worked on it were laughing saying,
you know, it's taken us a year to teach AI
what a noble looks like, and all we have to
do is look at it and we know straight away.
Speaker 3 (08:12):
That's one side of it.
Speaker 4 (08:13):
But the other side of it is, now that the
AI knows what a no ball is, it can do
a million games at once, a million balls at the
same time anywhere in the world. And that's a huge
change that's happening now. So things get easier, they get cheaper,
they'll get done in the cloud, but it means you
can do it way more effectively because you've finally taught
(08:35):
it how.
Speaker 3 (08:36):
To do it cold come, but a lot of work.
Speaker 4 (08:38):
Yeah, oh, I mean it was probably the worst worst
person that could happen to, reallyy.
Speaker 2 (08:46):
But on that look, ten thousand hours. That's so significant,
Sirian Tartar, and thanks very much for joining us, because
you know, to become an expert in something, you've got
to spend ten thousand hours, right, that's our humans operator
as well.
Speaker 3 (08:57):
Mate, So well done it is, isn't it. Well you
have a great Christmas.
Speaker 2 (09:00):
Doting and you as well.
Speaker 3 (09:01):
Thanks for your time, yere goy dear see it.
Speaker 1 (09:04):
For more from sports Talk, listen live toin News Talks.
It'd be from seven pm weekdays, or follow the podcast
on iHeartRadio.
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