Will Thames

swim, bike, run, tech

Race results

  • 20/05/2017 Ultra Trail Australia 100km 14:31:32
  • 30/04/2017 Pinnacles 18km 02:06:58
  • 19/02/2017 Mt Glorious 22km 02:46:21
  • 08/08/2016 Lake Manchester 22km 02:37:27
  • 29/05/2016 Brisbane Trail Marathon 05:56:14
  • 16/04/2016 Pinnacles 18km 01:56:30
  • 21/08/2015 UT4M 168km - DNF, stopped after 113km and 34 hours
  • 14/06/2015 Nerang 50km 06:17:28
  • 19/04/2015 Numinbah 2 Pollys 50km 06:38:07
  • 22/03/2015 Twilight (race stopped at 10km) 00:46:22
  • 08/03/2015 Mt Glorious 22km 02:40:29
  • 18/01/2015 Bunyaville Long Course 15km 01:40:13
  • 20/09/2014 Twilight Bay 10km 00:46:02
  • 13/09/2014 Glasshouse 100km 12:36:00
  • 03/08/2014 Brisbane Marathon 03:25:11
  • 19/07/2014 Kokoda Challenge 20:28:53
  • 17/05/2014 North Face 100km 17:51:14
  • 08/06/2013 Mitchelton Parkrun* 5km 20:23

Personal bests

  • 5km 20:23 Mitchelton Parkrun 08/06/2013
  • 10km 45:25 Bridge 2 Brisbane 01/09/2013
  • 21.1km 01:39:33 Twilight Half 23/03/2014
  • 42.2km 03:25:11 Brisbane Marathon 03/08/2014
  • 50km 05:52:27 Glasshouse 50 07/09/2013
  • 100km 12:36:00 Glasshouse 100 13/09/2014

Recent Posts

Graphing Performance

I’ve been getting into data analysis and training performance is a perfect target for such analysis.

Using pandas, along with matplotlib, NumPy and SciPy, I can graph the number of hours run

The python I used for this is

(Note that I had to use ImageMagick’s convert -trim +repage to remove unnecessary white space for display here - I’m sure I could have fiddled more withpyplot for a while to achieve similar results)

The data comes from my training summary, a drupal view I created to produce a CSV of my training log for such purposes (importing it into excel and playing it with it there was my original analysis method)