Last updated on October 27th, 2013 at 08:56 am
Estimated reading time: 8 minutes
302 gold medals were handed out in Beijing. Add to that 303 silvers, and 353 bronze medals, and you get a total of 958 medals won during the 16-days of Olympic competition.
When you consider that 10,500 athletes were in Beijing, then it’s actually quite interesting to calculate that on average, one in 11 athletes leaves the Games with a medal!
Of course, that’s not necessarily true, for two reasons. The first is that there are many multiple medal winners, like Michael Phelps, who won 8 all by himself. I guess one might say that his achievements account for 77 people who suddenly fall out of the calculation pool! On the other hand, team medals are counted as one, even though 20 athletes might win one. So, a German hockey team consisting of 20 guys each has a medal, but it’s reflected as a single medal, which might eventually cancel out the multiple-medal winners like Phelps, Bolt and co. So it’s interesting to note just how many medals there are on offer, once you make it to the Games, of course!
Today’s post is a look at where those medals went (not to individuals, but to nations), and to figure out just who the real Olympic champions are. China topped the medal table, by virtue of its 51 golds. That’s 17% of the gold medals on offer. But then China has 20% of the world’s population, so what does it mean? The USA top the charts in terms of TOTAL medals won (110 compared to China’s 100). But they are also the wealthiest nation, so should that be expected? And how do we evaluate Jamaica, Kenya, Ethiopia – smaller countries, with limited resources, who stole the show in Beijing in specific areas? That’s what this post is all about.
The Gold-rush: China’s remarkable rise
As mentioned, China was always going to be a superpower once it invested in sport. With a population of 1.3 billion to choose from, finding 100 medallists would not seem a mission impossible. But making it happen is no mean feat. For one thing, not all those 1.3 billion are in the “selection pool”, and massive talent ID is required to tease out those athletes, train them and prepare them for competition. And this is what China has done so well. An investigating into China’s sporting system in 1994 revealed that they have 4000 Coaches at the elite level! In a sport like rowing, for example, they have 1,200 full-time rowers, divided into squads of 60 to 70. That’s an amazing system, at a huge cost of course, but it’s partly responsible for producing the success they achieved.
But there’s something more to China’s success – it is not simply a numbers game. Take a look at the following table showing China’s medal hauls at the last 3 Games:
Two things jump out:
First of all, China’s ascent to the top of the charts has been achieved entirely by winning more GOLD medals. Their silver and bronze medal haul has remained unchanged since 2000. But they’ve almost doubled the number of golds.
Second, when you sum up the last 3 Games, China wins more golds than it does silvers and bronzes COMBINED! That’s quite extra-ordinary, given the number of medals won. In terms of pure statistics, you’d expect some kind of “normal” distribution of golds-silvers and bronzes. The table and graph below shows the USA and Australia’s returns, compared to the Chinese:
So it’s quite clear that the Chinese have figured out how to get straight to the top, win the Gold, and not win any silvers or bronzes! Of course, home-ground advantage might be a factor, because an athlete in contention may find that extra 2% that pushes them from second or third into first.
But I suspect there’s more to it than this. It’s still a vague idea in my own mind, but I think this is an indication of massive investment into specific events. There is no half-measure, no stone unturned. The Chinese identify their potential medal winner and invest so heavily in that person or team that they become a clear favourite. That kind of targeted approach produces people with clear gold medal chances, and is testament to massive investment in individuals. Second or third are not options, given the preparation that must go into creating a vastly superior athlete. That’s the only reason I can think of – the target is set, to win gold, and then they hit it. Pretty simple in principle, obviously very difficult in practice.
The underdogs: The small nations have their say
One of the more interesting means to analyse Olympic success is to convert the medals into a population per medal value. That is, compare the medals won PER PERSON, rather than an absolute number. This gives the “minnows” a chance to shine, because it’s quite clear that unless something is very wrong, a nation of 200 million SHOULD win more medals than a nation of 200,000! So let’s have a look at medals won as a function of the nation’s population (note: the population figures are up for debate, I suppose, but this is not a census, it’s just to make a point, so they’re accurate enough!)
The Bahamas, then, are the undisputed champions of population per medal. They regularly win 2 medals per Games, and with a tiny population, have a remarkable ratio. The USA and China suddenly fall off the top perch, and win only 1 medal per 2.7 million people or 13 million people, respectively. That places them outside the top 40 on the population adjusted table. South Africa, meanwhile, fare poorly in Beijing, thanks to the lonely silver we won. Previous Games were much better, and it’s understandable why fans are so disappointed.
Australia are the best of the “big” nations. It can be a little misleading doing this analysis, because the relationship between medals and size is not necessarily a straight line. So the Aussies, thanks to their intelligent investment in sport, make the most of what they have and do incredibly well given their relatively small size.
Just a note on this table – strictly speaking, one should try to “weight” the medals by awarding three points to gold, two to silver and one to bronze, and then work out a score. If you do this, then Jamaica come out on top, ahead of the Bahamas, courtesy their sprint domination and 6 gold medals in Beijing.
The source of big returns for small numbers?
So, to generalize slightly, the Caribbean islands tend to dominate this list. They are small, and yet regularly win a handful of medals, mostly in the sprint events (in Beijing, both the Bahamas and Jamaica won all their events in athletics). That is testament to the genetic potential of the atheltes in those countries. That’s a debate that was had in the comments section of one our recent posts, and it’s worth discussing again in the future – the genes for speed.
But for today, we’ll stick to management-related issues. I believe the key to making the most of this genetic potential (which I’ll assume exists, though I appreciate that it’s not clear-cut) is to facilitate competition with a focus on specific sports. Your emails have informed us that the school athletics system in the Caribbean islands is enormously strong, and this is the big driver of these medals.
Add to this a culture of the sport (which is why the school system is so strong), plus the existence of “aspirational heroes and role models”, and you have a mix that will ensure that talent comes through, despite relatively small investment and a small population. If anything, the smaller population helps the athlete come through. Lastly, the focus is not too broad – there is no swimming team, no fencing, no table-tennis to detract from limited resources. The eggs are placed in the basket that will produce results, and that means a much simpler system to run.
There is of course much more to it than this, but the post is running away with me now! So to spare some of your reading time, I’ll cut the discussion on the “minnows” short, and also say that we’ll pick up the Olympic Medal Analysis in a second post tomorrow. There, we’ll discuss medals won per dollar GDP, and figure out which countries get the best returns given their financial resources. We’ll also dissect the big failures (South Africa, mostly, since I’m here and biased that way!)
Join us then!
Ross
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