Rio Mid-Games: Gold Medals Per Capita So Far

Four years ago I blogged about the fact that medal counts are often reported on a per-country basis without regard for the population of any country. I’ve always thought that approach to be silly. The per-capita medal count is much more interesting to me.

So this morning I used Bing to get the latest count by country of gold medals at the ongoing Rio Olympics.  I included only those nations that have won two or more gold medals.

I then visited Wikipedia to get recent population counts for those nations.

I used all this data to determine the per-capita win per nation and ranked them.  The results are below.

Ranking Country Per Capita Ratio
(Descending Order)
Gold Medal Count Population Date Population Reported
1 Jamaica 7.34418E-07 2 2,723,246 31-Dec-14
2 New Zealand 6.37423E-07 3 4,706,450 16-Aug-16
3 Hungary 6.10811E-07 6 9,823,000 1-Jan-16
4 Croatia 4.77251E-07 2 4,190,669 31-Dec-15
5 Netherlands 3.52389E-07 6 17,026,640 16-Aug-16
6 Australia 2.48343E-07 6 24,160,100 16-Aug-16
7 United Kingdom 2.45738E-07 16 65,110,000 30-Jun-15
8 Switzerland 2.39762E-07 2 8,341,600 31-Mar-16
9 Greece 1.84196E-07 2 10,858,018 1-Jan-15
10 Cuba 1.77952E-07 2 11,239,004 31-Dec-15
11 Belgium 1.76637E-07 2 11,322,674 1-Jun-16
12 Italy 1.31871E-07 8 60,665,551 1-Jan-16
13 South Korea 1.18107E-07 6 50,801,405 1-Jul-16
14 Kazakhstan 1.12656E-07 2 17,753,200 1-May-16
15 Germany 1.10064E-07 9 81,770,900 30-Sep-15
16 France 1.04891E-07 7 66,736,000 1-Jul-16
17 Spain 8.61356E-08 4 46,438,422 1-Jan-16
18 United States 8.01855E-08 26 324,248,000 16-Aug-16
19 North Korea 7.91108E-08 2 25,281,000 1-Jul-16
20 Russia 7.50345E-08 11 146,599,183 1-May-16
21 Uzbekistan 6.33406E-08 2 31,575,300 1-Jan-16
22 Canada 5.53166E-08 2 36,155,487 1-Apr-16
23 Japan 5.51225E-08 7 126,990,000 1-Jul-16
24 Poland 5.20329E-08 2 38,437,239 31-Dec-15
25 Kenya 4.52934E-08 2 44,156,577 1-Jul-15
26 Colombia 4.09887E-08 2 48,793,900 16-Aug-16
27 Thailand 3.04279E-08 2 65,729,098 31-Dec-15
28 Iran 2.51689E-08 2 79,463,100 16-Aug-16
29 China 1.08836E-08 15 1,378,220,000 16-Aug-16
30 Brazil 9.68457E-09 2 206,514,000 16-Aug-16

You could argue that some country populations and/or medal counts are too small to prove anything other than an interesting anomaly.  Or not.  But there’s no question that Hungary and the Netherlands are very impressive, each with six gold medals for an overall per-capita rate that’s dramatically higher than, for example, Great Britain, the United States, and especially China, who – in spite of their very high medal count, is actually very far behind the other nations on a per capita basis.

These are the results presented graphically, which I created using Microsoft Excel, captured with the built-in “snipping” tool in Microsoft Windows: :

The Pareto Line seems to curve most significantly above Hungary and the Netherlands, which have the most striking performance in terms of size of population and count of gold medals.  Also – note how far back China ranks.  They might have a lot of medals, but they also have the largest population in the world.

To me, this sort of analysis is much more interesting than the simple medal count that doesn’t account for population.


  • Gold Medal Count as of 8/16/2016:
  • Country Population Data:

Amazon Web Services (AWS) – System Log

Here’s an easy way to review the system log of an EC2 Instance using Amazon Web Service (AWS):

  • Navigate to the Management Console (the starting point of everything in AWS)
  • Click “EC2”
  • Click “Running Instances”
  • Choose the instance you’re interested in by clicking the empty square to its right and turning it solid blue
  • Click Actions -> Instance Settings -> Get System Log

See below for what your screen should look like.

The result will be a pop-up window displaying the system log:

That’s it!

MySQL Migration: from Drupal to WordPress

I just migrated this Skere9 blog from a Drupal implementation at SiteGround to a WordPress instance at Amazon Web Services.  I’ve been a customer of AWS for three years and finally decided to move this blog to the same platform where I’ve been doing other work.  It’s cost-effective and AWS provides significantly greater control over numerous aspects of the deployment.

Note:  I have one author – myself – and I did not migrate any comments with this move.  I will add those later.

The Drupal version is 7.4.1.  The WordPress version is 4.5.3.

To migrate the blog posts, I took the following steps.

  • I temporarily installed phpMyAdmin at the Drupal site to export the “node” and “field_data_body” tables from Drupal.  (Note: this could have also been done using MySQL directly.)
  • I installed phpMyAdmin at the WordPress site to import the Drupal tables.  (Note: this could have also been done using MySQL directly.) I edited the phpMyAdmin config file to restrict access to phpMyAdmin to my own IP address.
  • I executed the script below via phpMyAdmin at the WordPress site to pull data from the imported Drupal tables and insert that data into the WordPress database.
INSERT INTO wp_posts
(       id
      , post_author
      , post_date
      , post_date_gmt
      , post_content
      , post_title
      , post_status
      , comment_status
      , ping_status
      , post_name
      , post_modified
      , post_modified_gmt
      , post_parent
      , guid
      , menu_order
      , post_type
      , 1
      , FROM_UNIXTIME(a.created)
      , FROM_UNIXTIME(a.created)
      , b.body_value
      , a.title
      , 'publish'
      , 'open'
      , 'open'
      , CONCAT('dr-', a.nid+100)
      , FROM_UNIXTIME(a.changed)
      , FROM_UNIXTIME(a.changed)
      , 0
      , CONCAT('', a.nid+100)
      , 0
      , 'post'
FROM     node a LEFT JOIN (field_data_body b)
            ON (a.nid = b.entity_id)
WHERE    type = 'blog'
ORDER BY a.nid

A few important notes about the above code:

  • I had fewer than 100 existing posts in the WordPress instance- it was a brand new installation.  This is why I only added 100 to the ID, to create unique identifiers.
  • I transformed the data formats from UNIX to plain text, as required by the source and target tables.
  • I chose to enter each value into the WordPress “postname” field with a “dr-” prefix – for “Drupal”.  But of course that could have been anything, or nothing at all – no prefix is required here.
  • The “_gmt” fields in the target database are being treated by my WordPress implementation as the same as the local time.  Presumably GMT refers to Greenwich Mean Time, comparable to UTC.  I’m in the USA eastern time zone (New York City zone – I’m in Washington, DC), so presumably the offset should be 5 hours but the existing WordPress implementation I have isn’t treating the times differently.  Therefore I didn’t treat them differently either – I made the “created” timestamp the same for the local and GMT entry, and did the same for the “changed” timestamp.

It all worked like a charm.