Monthly Archives: August 2012

North Korea leads the Olympics – Medals per dollar of GDP

Do richer countries perform better than poorer?  Is sporting prowess more important to a country’s leaders than feeding their population?  Or does this table reflect real sporting prowess. You be the judge.  North Korea (DPR Korea) leads the medals per US$100 Billion of Gross Domestic Product (2010 figures from the UN statistics), Moldova and Mongolia are not far behind.  New Zealand ranks 17, still well ahead of Australia, 31. Oh well.

Number of medals won per US$100 Billion of GDP by the end of day 9 in the London 2012 Olympics

New Zealand surges to Silver

On the seventh day they did rest – I think not.  Aotearoa a land of couch potatoes – absolutely; sitting down wins medals!  Super cyclists and outrageous rowers took New Zealand’s medal tally to six on the seventh day of the London Olympics.  All medals have been won sitting down (and two-thirds of them going backwards).  New Zealand has surged to silver position on the medals per capita table; now with more than one medal per million population.  Only Slovenia is ahead with its 3 medals and a 1.48 Medals per million score.

But wait, here come the Aussies, jumping from 7th place to 4th place overnight as they also picked up 3 more medals.

In yesterday’s blog I raised the issue of outliers – little countries with one great athlete could do leap ahead.  To be eligible for the medals per capita medals should countries win medals in more than one discipline (say 3)?  Should there be a minimum number of athletes who win medals (say 3)? How do we deal with outliers – give me your ideas.  Also, don’t forget to tell me if you want your country represented on the graphs.

Medals per capita – day 7, London 2012

Which is really the best performed Olympic country?

Over at Statistics New Zealand the folks have done a great job in producing an alternative medals table for the London Olympics.  They are showing the number of medals won per million population.  They have kindly shared some of the data with me (thanks people) so that I can produce a few graphs and tables of my own. First a message to TVNZ and TV 3 and PRIME TV:

It is really, really, REALLY, boring to have presented a table of which country has the most medals every night.  It is almost as boring as those worthless tables of currencies and stocks you also insist on presenting for your sponsors.

Now, I have that off my chest, let’s have a look at what is going on…If you’ve had a look at the alternative medal table you will see that today, end of day 6, New Zealand is third on the table with a 0.69 medals per million population (i.e. 3 medals divided by 3.7 Million people). It is apparent to any unbiased observer that the number of medals per million is a much better indicator of sporting prowess than raw numbers of medals.  It is, though, subject to a few anomalies which I hope to point out over the next week or so.

In the meantime I’ve produced two graphs to demonstrate what is happening day by day.  I shall update these a couple of times over the rest of the Olympics.   The first shows the number of medals per million for New Zealand, Australia, China, Great Britain, and USA.  New Zealand started slowly and is accelerating nicely.  The second graph is perhaps more interesting as it shows the ranking in terms of numbers of medals per head of population.  At present Slovenia with 1 medal and a population of a little over a million has the number one ranking and a medals per million score of 0.99.  A couple more medals and New Zealand may pass this.  Of course, if a country with a very low population, say  Nauru, were to win just one medal then their score would be about 100!  This is take home lesson #1.  Look at all the numbers and look out for outliers. Would this make Nauru the country with the greatest sporting prowess?  No.  We would need to look at historical data over many olympics to be certain of that.  Another aspect of the second graph is that it shows how some country’s ranking is trending up and down.  This is influenced by their daily performance, but also by other countries entering the table.  What should be expected as each day goes on that each country will trend towards their final ranking – perhaps bouncing around above and below it (especially if they are “mid table”).  This idea is sometimes called “regression to the mean” and it is very important in statistics because it tells us to be very cautious about putting too much emphasis on the first data we collect.  For example, if every doctor in the country starts collecting data on the number of meningitis cases they see each week. In the first week some may see none, others 1 or 2, but it is quite possible that one of them sees 4.  Could this be an epidemic in their area?  Well a wise doctor would continue to follow and see if there is a trend with the next week or two’s data before jumping to conclusions (although the “precautionary principal” may apply and health boards would be notified).

Have fun following the Olympics.  If you want your favorite country added to my graph – just ask.  In the meantime, check out Statistics NZ’s excellent presentation of the top 10 ranked countries each day.

Number of medals won at the London 2012 Olympics per million population day by day.

The rank (1=top) of the number of medals won per million of population at the London 2012 Olympics


$6,126,820 has been sitting on my fridge for the last two years. I aim to raise this over 20 years so as to continue my research.  Yes – I confess, I am the Six million dollar man (Historical reference for those over 40).  Sounds a lot of money, but let’s put this in context.  Because I am “research only” staff, I must raise all my salary and expenses, so the calculation was the sum of my salary, a salary for a part-time research assistant (2 days a week), overheads on both our salaries at a rate of 108% (the rate my university expects from me) and about $20,000 a year for a few research expenses.  In other words, about $300,000 p.a.

A few comparisons from government funding

Teacher: $164,000 p.a.   New Zealand spends about $7000 per secondary school pupil.  Apparently there are 23.5 pupils per year 9 student.

 Olympic athlete:  $150,000 p.a.  According to Prime TV, the NZ government spent $108,000,000 sending ~180 athletes to the current Olympics.  Assuming this was spread over 4 years, then this is about $150,000 p.a per athlete.  Of course, many also have corporate sponsorship.

I wonder what a mid level manager with a part-time secretary in the ministry of housing costs?  I can well imagine it passing $300,000.

The Six Million Dollar Fridge

The Six Million Dollar Fridge

Is what I do worth two athlete’s olympic performance?  Is it worth more than an average year nine teacher.  Perhaps not for me to say. This is not to say the government should not put money into the athletes or teachers, merely to point out that if I were to raise the money from government science funding such as the HRC or Marsden, then this would be my relative value to NZ according to the politicians who divide up the budget.  The reality is that I am very unlikely to raise this money from government sources.  In the last two years I have raised about $420,000 dollars of which $300,000 is from governement funds via the Marsden fund (thank you) and a little from the University of Otago Research Grants. The rest is from the Australia and New Zealand Society of Nephrologists. Unfortunately, it is about $200,000 under budget, so I no longer have a research assistant (she was very good and is sadly missed). If I were to reach my goal via governement funding I will need to get a gold medal (an HRC grant or Marsden grant) every two to three years.  As these have success rates of about 7 and 12% respectively, this is a very big ask.  So, how shall I raise the dollars?

The Plan

First and foremost I shall continue to put the bulk of my time into being the best scientist I can, otherwise there is no point! My skills are in science not fund raising.

Second, and despite what I just said, I shall look for innovative ways to raise money.  Siouxsie Wiles sojourn into the world of crowd source funding was inspiring, if not a little daunting. Perhaps this sort of innovation on a larger scale?  For that I need to find the right people – entrepreneurs and fund raises who will help me find the people looking to donate to a good cause.  Maybe I will write Apps or ebooks? No stone shall be left unturned.

Third, expand my connections to other research groups here and overseas.  I’ve already begun this – I now have an honorary position with UNSW in Sydney.  So far, no money has come with the extra work, but it is worthwhile work and I certainly would like to contribute to more such projects.  As I am a data analysis person the mantra is –give me your data and I shall massage it into a story worth telling.

Fourth, corporate sponsorship.  Yes, I will wear their jacket and paint my car if they so desire.  In medicine corporate funding is a tricky business.  It is important not to be seen to be biased.  As I am not a medical doctor, I have the advantage that any sponsorship could not influence my clinical practice (I don’t think it does for most medical people anyway). However, because I am not a medic, pharmaceutical companies and the like are probably less likely to sponsor me. But if I don’t ask I won’t know!  So far I have had a good relationship with three biomarker companies who have measured specific protein concentrations for myself and my colleagues using their own assays – no strings attached.  Essentially, I contribute to their knowledge base and they contribute to my research.  Unfortunately, there is no cash flowing for salaries yet.

Fifth, I shall remind the university that my contribution to their PBRF funding is substantial and some kind of retainer wouldn’t go amiss.

Sixth, I shall continue to talk with politicians about the lack of public funding for science.  I began this in 2008 and have had several good discussions.

Finally, I shall not totally give up on grants just yet, but I shall be judicious about which ones I spend time applying for.