I wanted to make a chart that told the following story: Although the top athletes make a lot of money and those are the athletes you see on TV, if you care about how much money you make, you’ll have better odds becoming an accountant.
To tell this story, I thought I would show the average earnings of pro athlete and accountant and use error bars to show the range. The range would demonstrate that the downside to accounting is much more tolerable than the downside to being a professional athlete. Well, this chart sucks.
I have to use a logarithmic scale on the Y axis to make this even readable, but it dilutes my point that the average athlete earns far less than the average accountant. I really need to visually demonstrate that the vast majority of athletes make very little, but I’m not sure how to best show it. I’m trying to dash the hopes of a 12 year old, so you know where I’m going with this. Let’s say this data is accurate
| Income | Athlete | CPA |
| 1 | 10 | – |
| 10 | 2 | – |
| 100 | 2 | – |
| 1,000 | 50 | – |
| 10,000 | 25 | 1 |
| 100,000 | 5 | 98 |
| 1,000,000 | 3 | 1 |
| 10,000,000 | 2 | – |
| 100,000,000 | 1 | – |
| 100 | 100 |
It’s not, but lets say it is. How you would chart it to convince a pre-teen to eschew the hoop dreams and start studying algebra?
Some sort of bellcurve comparison?
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Although the top athletes make a lot of money and those are the athletes you see on TV, if you care about how much money you make, you’ll have better odds becoming an accountant.
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Why not just show that: What are the odds of becoming a pro athlete? What are the odds of becoming an accountant? What’s the average salary of an accountant? What’s the average salary of an athlete? Maybe in time series: pro-athletes have shorter careers. How about number of debilitating concussions received as a pro-athlete vs. those received as an accountant. If they weren’t pre-teens, you’d probably have to contend with this:
Number of Women Interested In You By Career
Accountant: |
Athlete: ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
But feel free to wash that part because it doesn’t support your argument:)
Actually, I think the ‘odds to become’ idea would be pretty meaningful. I bet you probably have a better chance of becoming a fighter pilot in the navy than you do a quarterback in the nfl.
I think a tornado type diagram would work best
Instead of graphing the potential and average earnings of the two careers, chart the estimated number of people with aspirations to play basketball versus those that could possibly make it as a pro basketball player versus the same with those who could make it as an accountant.
Call me crazy, but if this isn’t a good opportunity for a 3D exploded pie then I don’t know what is.
Another factor to include would be interest shown by the opposite sex. Which is strong no matter how well paid an athlete you are but you have to be a *really* well paid accountant…
Why not use a box and whisker plot and keep the logarithmic scale. Box and whisker plots are grate for showing and comparing distributions and I think would drive your point home well (and your 12-year old should be familiar with box and whisker plots already).
The problem is pre-teen is right even in your goals: the mean income is $1,238,002 for athletes and only $108,100 for CPA.
Won’t work. No 12 year-old believes they’re average, they’re one in a million.
Most adults don’t understand it either, even when Life has smashed them in the face with the Average-As-Heck stick, repeatedly, for decades.
Look at how many Lottery tickets are purchased, or how many no-hoper hopefuls self-deludedly enter “America (or wherever)’s Got Talent”.
Show him (or her) the numbers, and all you’ll get is “So 1% can earn $100m? Cool!”
Maybe the better motivation is that once they’ve earned those millions they’ll need algebra to help them stop accountants stealing it?
Frankly, I’d be a little disappointed if either of my kids announced a vocation for accountancy at that age. That choice comes after our first few encounters with the aftorementioned Average Stick. Well, I chose programming, but the same principle applies. At 12 I was going to be an astronaut, IIRC.
;-)
I’d make sure I spell “Athlete” correctly on the chart label for starters.
I agree about the curve approach or a histogram–show the number of athletes in each income range but keep him away from the book Outliers or (as Mike suggests) he’ll be certain the he’s The One.
You might also do a chart of heights in the NBA. If he’s going to be 6’10? then great, but there are not many Spud Webb’s in the NBA historically.
You could also appeal to the “once you retire from the NBA, you don’t want to be like all the poor sap ex-pros who get ripped off because they have poor judgement and worse math skills.”
None of this logic will work, and rightly so. 12-year-olds should be allowed to dream bigg. It’s the right of the young, as long as they keep their grades up too.
I wouldn’t
Just encourage them to try the hardest at whatever they do and don’t give up
You need your kids to be well off enough to look after you in your twilight years.
Hui, you just proved yourself not to be wise in excel only…. I could not agree more with you…..
Charting incomes only works if the intended audience cares about remuneration. If they care more about fame, you’re sunk: name 5 well-known CPAs who aren’t also either MBAs, lawyers or government investigators. That is, 5 CPAs famous for being CPAs rather than famous people who also happen to be CPAs.
And just to throw a monkey wrench in the works, compare the income after deducting student loan payments between CPAs and high steel construction workers who generally have no student loans to pay off. Actually, probably not relevant to Omaha and environs, crane operators who load container ships make 100K – 200K and don’t require much if any college, but they have to have very good spatial reasoning and hand-eye coordination. Unfortunately, sports may help with that.
I wouldn’t attempt to dash anyone’s hopes. When I was 7 or 8 years old I wanted to play guitar. My parents thought it was just a phase (you know … he’ll grow out of it). How many times did I hear that there’s no money in music, the career is short lived and besides, lessons were too expensive.
So I bought my own guitar, taught myself to read music, learned chords, joined bands, won awards, and 40 years later I still enjoy playing. I was never in it for the money. I just wanted to play guitar. This has certainly helped to balance my life away from my career.
I wouldn’t attempt to make it an either / or proposition – this can easily be an AND operative. Be a great athlete AND whatever else. Point to the short career span of an athlete and ask what he’ll do when he’s done being a pro? Point to the number of athletes who also have major business and investment interests. How would he manage them without an education?
Well, there is a similar story…….. A Statistician was to cross the river. He took the average height of family members and it came to 5′-4?…….. He knew that the average depth of the river was 5′-0? and decided to cross the river. Well……..averages become irrelevant at a depth of 8′-6?
The chart would make your point better if it left out some of the problematic data. In the example below, I have adjusted the earnings scale to remove the distraction of those $100m earnings :)
Also, it is probably more understandable if we chart the cumulative percentage of people and their average earnings. Like so
http://www.edferrero.com/content/AthleteVsCPA.png
1. Bubble chart – but you need more data.
2. Dick you’re a horrible man!;-)
3. Also, surely there are a load of accountants that make more than 100K?
You could show him something like this, talking about the ~30,000 kids born in 1975 who played hockey in Ontario. Of that group, 232 were drafted to play n the Ontario Hockey League (the highest rated Junior league, and the most common route to the NHL).
* Out of those 232 players drafted to the OHL, only 105 ever played one game in the OHL.
* Out of those 105 players, only 90 finished their full 3 or 4 years of eligibility in the OHL.
* Of those 30,000 players, only 42 played NCAA Division I hockey! Remember too that U.S.scholarships are not the large educational packages that have been offered by NCAA schools in the past (see more information below). The following “1975” players had either full or partial NCAA scholarships.
* There were 56 players from the “1975” age group that were either drafted or signed by a National Hockey League team (by far the most of any birth year Ontario has experienced!). Fourty-eight (48) of those 56 players were drafted by NHL teams!
* Of the 48 drafted players only 39 signed contracts with NHL teams. Eight players signed as free agents after going un-drafted as NCAA or major junior players.
* Of the 48 signed players, only 32 have seen action to date in an NHL game.
* Of the 32 players with NHL experience, only 15 have played more than one (1) full NHL season!
* Of these 32 players, only 21 were active in the NHL as of April 1, 2002.
* Of those 32 who have played an NHL game to date, only 18-20 will earn a second contract with an NHL team. About half of those players earning second contracts will see them finish that second contract with an NHL team. The remainder of the 56 players will toil in the minor pros in the IHL, AHL, ECHL or Europe.
* Of the 32 players who have seen action in an NHL game, only six (6) have qualified for the NHL’s Player Pension (minimum 400 games in the NHL!).
I don’t think some of the numbers quite add up – but I am just cut / pasting. The original is at:
http://www.omha.net/flash.asp?page_id=242
Forget about making a chart, the table you posted works very well.
What a thread. I have no suggestions. My kids are just one year old and they seem to care so darned little about numbers or stats.
You should convince the 12 year old to become an accountant in a sports club :D But he may hate you.
Btw, I like the table better. It brings the point to notice. Also, the cumulative chart by Ed.
Second the suggestion of using a “frequency plot” or “probability plot”.
I would make a normal probability plot. We use these in hydrology a lot, since we’re used to dealing with high outliers and skewed distributions, which is exactly what you have here.
I like what you’re doing, focusing on the most likely income based on probabilities. Good application of the expectation operator:
http://en.wikipedia.org/wiki/Expected_value
Not much to add except that it’s interesting how many assumed the 12 year old to be male and for those who didn’t they tended to assume it as primary, bar 1 person I think.
Plus measuring interest from the opposite sex is assuming the norm to be a shallow relationship. Boo hiss.
“Plus measuring interest from the opposite sex is assuming the norm to be a shallow relationship.”
It’s not easy spotting humour online, is it?
I’m taking a different approach:
Aged 30 or so can you move from failed sportstar to Accountant? yes, might take some work/time but in general it possible
Aged 30 or so can you move from bored accountant to sportstar? no.
My 10 year old wants to be a sports pro – go for it I say, clear up when you are 30 if it doesn’t work out. But he still has to do his homework.