- Hey, everyone, Daniel Ricciardo here.
And we are back for another episode of
McLaren Substitute Teacher: life lessons
in the art of Formula 1.
Even better, you don't have to sit in an exam for this one.
So today's lesson is brought to you by Chris
who works in our engineering department.
Chris is actually very, very intelligent.
So please pay close attention and shh,
no talking at the back.
- Hi, I'm Gemma.
Allow me to introduce today's McLaren Substitute Teacher
It's Chris.
Chris, tell us what you do.
- Hi, my name's Chris Alexander.
I'm a data applications engineer for McLaren Racing.
Today's topic is how data can help you predict the future.
- Oh, intriguing, so does this involve crystal balls?
- No, but it does involve tennis balls.
- Oh, all right, then.
So what are you going to serve up?
- Well, firstly, we're going to take a look at
why it's so important to have good data
and how that helps you make a prediction.
Then we're going to see how that technique
is applied to Formula One
and how it helps us improve our performance.
And finally, we've got an experiment
for you all to try at home.
- Well, that's all the data I need.
Let's head to the classroom.
Okay, Chris.
Oh, I'm guessing you got distracted or something,
or did you just run out of blue ink?
- No, Gemma, it's all part of the learning intention.
As you can see, I've started drawing a graph.
On the X axis is time moving forwards.
But you don't have a whole lot of information.
I've deliberately withheld some data.
Can you predict where the line's going to go next?
- Ooh, okay, so everybody watching now
grab a pen and some paper and join in.
Draw a pair of X and Y axis and then
add in where you think the blue line will end up.
I'll do one too.
- Oh, here you go.
- Thank you.
There.
- Excellent.
So to help you make a better guess,
I'm going to give you some more data,
and some more of the plot,
and some numbers, too.
Okay, here we go.
- Oh, okay.
So now we have an X axis that goes from zero to one second
and a Y axis that goes from zero to three,
although we don't know the unit,
so it could be distance or it could be strawberries.
- It could.
So how has this extra data affected your guess?
Did you get it right or wrong?
How about drawing out a new guess for yourself
predicting where the line will go.
- Okay, then.
So either sit there, feeling very pleased with yourself
or have another go.
- Oh, I think you're going to need this.
- I need to have another go, okay, thanks.
Okay.
- Excellent.
Right, let me give you this final piece of data.
This chart shows you the height of a tennis ball
off the ground when you throw it up in the air
and let it bounce from the floor.
Gemma, would you like to demonstrate?
- Oh, yes.
How was that?
- You're ready for Wimbledon.
- Ooh, thanks.
- How has all this extra data
affected your prediction of what the chart will look like?
What do you think the chart would look like now?
- Hm, okay, come on then, everyone.
Back to your graphs.
- So your chart would show something like this.
- Oh.
- Exactly.
So you now have all the data from this experiment
and an accurate graph showing the height of the tennis ball.
With just a small bit of data at the beginning,
your prediction probably wasn't all that accurate,
but as you collected more information,
your graph got better and better
until you had a really accurate prediction
by the time we got to the last clue.
This is a really important aspect of predicting the future.
The better data you have,
the better your prediction will be.
This is why it's so important for us in F1
to collect as much and as accurate data
about the car and its environment as possible.
- Just in case one of the drivers
throws a tennis ball?
- Well, anything can happen in Formula 1.
And it usually does.
We're going to do another experiment,
but change one of the pieces of data slightly.
Can you predict the future of the chart
if instead of throwing a tennis ball,
you throw this watermelon instead?
- Okay.
Now I know why we're not on the Boulevard for this video.
All right, everyone, let's see your graphs
for the flight of the watermelon.
- Now you've drawn out your prediction.
Time to see how it matched up to reality.
All right, go on then, Gemma.
I know you want to.
- I really want to,
but we can't do it here in the classroom.
Let's go outside.
You ready?
- I'm ready.
Oh, dear.
- Wow.
- Well, I'm glad that exploded.
- Yeah.
That was amazing.
I love that.
- As you can see from the action replay,
no bouncing here, just a clean-up nightmare.
Your chart should look something like this.
No bouncing anywhere in sight.
This goes to show you
that once you've got a good set of data,
you can use that to make predictions
about what might happen if you change the experiment.
Here, we were changing what we were throwing
from a tennis ball to a watermelon.
You may have predicted the result.
No more bouncing, but certainly a lot more mess.
- Yes.
- In F1, we can use our existing data
to make predictions about other changes.
So for example, if we change the front wing flap angle,
what happens to the aerodynamics and how the car handles?
- Okay.
Now, Chris, you don't happen to have any
pumpkins around, do you?
Okay, so we've made some predictions,
but why is predicting the future
so important to the McLaren team?
- Well, one really good example is predicting
how our cars tyres are going to behave.
For example, in F1.
- All right, well, to help us do that,
I'd like to introduce you to Serena.
Hi, Serena, what do you do at McLaren?
- I'm Serena Potts and I'm a tyre performance engineer
for the McLaren F1 team.
I analyse how the tyres are behaving
and help make predictions about them
to help the team get the best possible result
at each F1 race.
- Wow, so how does understanding
tyre performance affect that?
- Well, the tyres are the only point of contact
between an F1 car and the ground.
So all of the hard work you put into the rest of the car,
including aerodynamics, brakes, engine, cooling, suspension,
and electronics can only have an effect
if you're able to translate these changes into energy
that can help the car accelerate, brake,
or turn around a circuit.
- Wow, so really important, then.
And how does predicting the tyre performance
affect the F1 race?
- The performance of the tyres changes as they get older.
This means that your lap times will begin to get slower
as the tyres wear down.
So we need to predict how quickly they will wear
and therefore how much slower the lap times will be.
It's a bit like being properly dressed in the winter time.
If you've got on tonnes of layers,
then you're able to run around and do activities.
But if you decide to take off your hat
and then your mittens, you might begin to cool down.
All of a sudden, you don't have the energy
to run around anymore.
It's the same deal with tyres.
As tyres begin to wear away,
they begin to lose the ability to retain heat.
And this means they start to lose performance.
And this results in slower lap times.
Then there's the impact on pit strategy.
Once the tyres begin to wear and age,
it can be faster to pit for a new set
than carry on with the used ones.
So you have to work out the trade-off
between putting on a fresh set of tyres
versus the time lost driving through the pit-lane.
But that's actually a job for our strategy team.
They consider other factors like the weather as well.
- Amazing.
Thank you, Serena, for that insight.
So, Chris, using weather data to predict conditions
can make all the difference.
- It really can, Gemma.
F1 is all about the tiniest of margins.
You need every advantage you can get,
especially when you're racing at over 210 miles per hour
as we do at Monza.
It's the difference between serving an ace
and splatting on the floor like a watermelon.
- So we know it's important,
but how do McLaren predict the weather?
- Well, McLaren is a race team and not a weather forecaster,
so we get data from the best available sources
and use that instead.
We employ what's called an ensemble model,
which brings together multiple sources of data
to produce a more precise picture.
Different weather models have different strengths,
and we can capitalise that to our advantage.
- So you crunch all the data
and make your own predictions?
- Exactly.
We make sure we cover a lot of bases.
All our sources give us
the same type of data about the weather,
but it's predicted in different ways.
And this gives us confidence in the accuracy
of the overall prediction.
Of course, the closer you get to an event,
the prediction improves in quality.
And more often than not, you have a better idea
of what the situation's going to be.
- So you can predict a new addition to the trophy cabinet.
- That's exactly what we're going for.
- Now, before we go,
we're going to set you a little exercise.
A chance to practice your data skills
and experiment with a predictive test.
Ha, see what I did there?
- I did.
Imagine there's a race happening in your area
a week on Sunday.
Say, lights out at one o'clock.
That's less than two weeks away.
Can you find three different sources
for a weather forecast for that date?
Then, can you combine those together
to predict the weather at the time of your pretend race?
- And what happens if you monitor
those weather sources over the next couple of weeks?
If you check them every day,
how does the forecast for your race time change?
- Maybe you could chart the different predictions
on a graph to see if you can spot a trend.
- And once it gets to the time of your pretend race,
you can maybe pop outside
to see if your predictions were correct.
- But if it's raining, you can check from inside.
- Ah, yeah, that's probably a good idea.
So send us your data and your analysis
by posting a response in the comment section
on Twitter and Instagram using the hashtag
#McLarenSubstituteTeacher.
And make sure you reign in your instincts
and do some blue sky thinking.
Until next time.
Bye-bye. - Good-bye.
- Oh.
You didn't see that one coming.