Instructor: So I wanna show you an example
of finding the So What in a data set.
And what I'm looking at here is a data set
of Olympic competitors.
And this is from 1896 all the way
through the modern Olympics, every Olympic competitor.
And right now they're sorted by ID.
This is like an ID column in the data set
where this came from.
I've filtered out a certain subset of the data.
I'm not gonna go into why or who they are exactly,
except to say this.
When I was investigating this data,
and you can see I have the person, the ID,
the name of the person, their sex age, height, weight,
team, and like an abbreviation for their country,
which games they competed in, in sports,
and whether they won to medal or not.
And I was looking at this data
and look, there's an age column and this is
what I filtered out.
Essentially, there are some people
who we don't know their age,
so I removed them from the data set.
But if I sort by age in descending order,
oldest to youngest, I found this insight.
Look at this.
The oldest competitor in the Olympics, in history,
was 97 years old.
What?
And there's a 96-year-old, and a bunch
of 80-something-year-olds and seventies, like,
so I just discovered this in the dataset.
So I decided to explore age in the Olympics.
That was like the premise,
like just the category of exploration.
And I, first I sorted.
I did all the things I recommend you do
in data analytics.
Some summary statistics and some sorting.
And what I quickly discovered
and I'm gonna go over to the pivot tables here,
was I looked for distribution of ages, okay?
And listen, there was one 10-year-old,
a bazillion years ago, maybe not a bazillion,
I can't even remember when that one was.
One 11-year-old, a bunch of 12-year-olds,
but as you can see, the distribution
sort of fattens out and you have a bunch
of 20-something-year-olds,
and then it starts to shrink again.
And so essentially by the time you hit your thirties,
mid thirties, the numbers get pretty darn small.
Once you hit 40, they really get small, okay?
And so the basic premise then became,
as someone who is over 50 years old,
I was wondering whether or not, technically,
my Olympic dreams were over.
is there any chance I could ever compete
in the Olympics given that I'm over 50?
So that became the premise.
And I explored this data in a whole bunch of different ways.
I found a lot of interesting insights.
I looked at every single sport that's been around
for at least five Olympics, counted the number
of athletes who competed in each one of those sports.
Found some interesting insights like, golf
and karate haven't been around for five Olympics.
And some other sports had been around,
but maybe just barely for over five.
Some that were around for five, or maybe not five,
I can't remember, in earlier years, but aren't around now.
Long story short, I also realized
that a lot of my data wasn't that good
before 1980, so I sort of narrowed the focus
into the modern age of 1980 and beyond.
Discovered some interesting patterns
when I looked at the average age per sport.
In alpine skiing, the average age is 23 point something.
In like gymnastics, they're very young on average.
Rhythmic gymnastics, they're like 18, 19 years old.
Other sports, higher average age.
I discovered some other insights as I played with the data.
I found some sports where like, team sports,
where if they don't have at least some older people
they're less likely to win.
All kinds of interesting things.
But eventually what I really narrowed in on was
if you want to compete in the Olympics
and you're beyond the age of 50,
it's all about equestrianism.
That's really what it came down to.
There are a couple of others, for instance,
some of these shooting sports like, archery
and shooting specifically, actually maybe even not archery,
this is a different table.
But long story short, the primary issue was all
about equestrianism.
And so I investigated the data a whole bunch
of different ways.
The end result was the data story that I created
that you can see here.
Are your Olympic dreams over at 50, okay?
And I'll just show you the data story real quick.
And you can find this if you Google
for it or you can see the URL above.
It's a journalistic sort of a story,
"Age is just a number."
The idea is that your best years
in whatever your profession might be, might come later
in life, but not so much for athletes, usually.
And so then I show that distribution
of athletes ages, right?
So very few athletes over the age of 40,
and especially over the age of 50,
as I point out in the story, you'll notice that I stop
at the age of 74 because what I forgot to mention,
these 80-plus-year-olds in the data set,
these were all competitors in the art competition.
Yeah, well look at this one.
Winslow Homer, very famous artist,
competed in the Olympics when he was 96-years-old.
The art competition literally was a competition.
You could win an Olympic medal as a sculptor,
as a painter, et cetera.
But that competition ended in, I think the 1940s,
or maybe the '30s.
And you know, obviously if you're old,
you can compete in that.
So I sort of took that out of the data set
for this data story.
So that's why the ages end at 74.
But yeah, a 74-year-old competed, not as an artist.
Anyways, when we remove the art competition
from the bit, I looked at just a few little pieces
of the data.
The average age by sport making the point
that a slight majority of sports,
you're more likely to win a medal
if you're above average age than
if you're below the average age.
But five of those have had a single 50-plus medalist
since 1980.
So I'm sort of taking that general story
but then narrowing in on it to say yeah, but, right?
In more recent years, only a few of those are
really relevant for the story that we're talking
about here today.
And I looked at the data a few other ways.
I'm not gonna show you this entire data story
but the basic idea here is,
I discovered some patterns, right?
So in winter sports, you're not gonna compete
if you're beyond 50.
It's all about the summer sports.
And like I said, it's really equestrianism
and a couple of others.
Long story short, I also showed
how equestrian is the most geriatric sport,
by far, when you look at the average age among competitors.
And also the idea of all the quote, unquote,
"Geriatric Olympics" delivered or won,
the vast majority of them are in equestrianism
a few in some other sports, and on top of that,
some people win multiple medals, even as older competitors.
I then have a final data story, or part of the data story,
where I give you the ability to read
about each one of these older medal winners
and learn a little bit more about them individually.
Long story short, the true So What came about
when I realized that A, this is a story
about me really, could I compete in the Olympics
being above the age of 50?
What are the interesting little tidbits
in the data that are about that?
And some of the other insights that I found,
such as the fact that team sports,
you're slightly more likely to win a medal
if you have a slightly older average age,
wasn't really the story that I was talking about.
That wasn't really a true So What for my story.
It's interesting.
It's definitely, could be its own story
and probably should be done as a story on its own,
but it wasn't really relevant for my story.