The document discusses the author's background in engineering and lack of athletic experience. It then explains how the author applied a quantitative and technical approach to training for an Ironman triathlon using various sensors and metrics. This included monitoring parameters like power, heart rate, speed, and lactate threshold during swimming, biking, and running. The author analyzed the collected data, which provided advantages over other athletes who did not take such a data-driven approach or lacked necessary sensors. In the end, quantifying performance helped the author better identify effective training practices.

1. Quantitative triathlon Making Recreation Productive

3. MS, Computer Science

4. 7 years embedded systems work

5. 5 years CDMA phones

7. MS, Computer Science

8. 7 years embedded systems work

9. 5 years CDMA phones

11. Not run in 5 years

12. Never learned to swim

14. Two Approaches to training Traditional Technical Swim a lot Bike a lot Run a lot Swim tech: Limited GPS Limited Heart Rate Bike tech: HR Monitor GPS Power Meter Run tech: HR Monitor GPS Foot pod (Nike+)

15. Six Sigma – Belts Earned, Lessons Learned I worked for both Motorola (creator) and General Electric (largest user) You must quantify and measure a parameter in order to control it Six Sigma is mostly worthless

16. Important Parameters for Cycling Speed Most important! Improvement here is the goal Difficult to improve by itself, combination of lots of factors, both internal and external Power to Weight ratio (Strength to Weight) Most accurate predictor of cycling performance Measured in Watts/Kg Lactate Threshold Power (FTP, CP30) Directly measured output of your body Lactate Threshold Heart Rate Directly measured reaction of your body

17. 2010 Average Weekly Watts/Kg

18. 2010 Average CP30 (30 Min Critical Power) CP30 is a very good approximation of Functional Threshold Power (FTP)

19. Heart Rate I don’t track my Lactate Threshold HR very closely I use HR as a guideline for when I don’t feel good – it’s a backup for my power meter. Mean Maximal HR, 2010 Running

20. Miscellaneous Data Analysis

21. Miscellaneous Data Analysis

22. Summary Most Athletes don’t own power meters, too expensive Those that do typically don’t know how to analyze the data This gives me an advantage, even over those who are more talented than I am By quantifying my performance, I am better able to identify effective training practices chris.teague@gmail.com http://breakneckgeek.blogspot.com/