On my way to computing how many people I actually passed, I found out how many people I would have passed if everyone started at the same time (i.e. no wave starts). It turns out I would've passed over 500 people, the third highest number of kills in this theoretical scenario.
My actual number of kills is much less, mainly because I was in the second wave after the pro/elite. I had less opportunity for kills. The majority of people didn't start the race until after I started. At the same time, my number is inflated because my swim time was so bad. I was passed by athletes who started 15 minutes later.
When I first started training for the triathlon, I was surprised at the range of both age and body types of its participants, but it makes sense that the breadth of sports - swimming, running, biking - allows for a larger variety of physiques and fitness levels.
This was most evident on race day, where almost every age 15–75 was represented. It's both inspiring and humbling to be passed by somebody your parents' age. In fact, I met a young woman who was racing with her mother for the third year in a row. How much does age matter really? It turns out, it matters, but only a tiny amount. It was a statistically significant predictor, but would only explain less than 2% of the variance in finishing time.
What makes triathlons interesting as a race is that all three sports, and their transitions, matter. It's about the challenge of training for all three and then completing one after the next. But what segment of the race best predicts your rank? All the data screams, “Running!” It has the most variance (sd = 17min), meaning the gap between the slowest and fastest runner is largest. Whether we're looking at time or rank, the running leg made the most difference.
Not surprisingly, of the three sports, age affected running the most. (And surprisingly, age does not significantly affect the bike portion.) One factor in this might be that the later waves (older folks) experience more heat, slowing them down in the run, especially.
Keep in mind that because the run was the biggest predictor of rank, it doesn't mean that that's where you should train. I, being in the 14
Below I've included all the data and code used to create these visulizations. Want to see who exactly you passed? Want to apply this kind of visualization to another race? Think younger people are better at transitions? See for yourself.
1. Official Lavaman 2015 race results
2. Official Lavaman 2015 race results, extended (no longer online)
3. CSV of filtered race results, with additional columns of computed kills
4. Python code that takes (2) and outputs (3).
5. D3.js code to make the race visualization and scatterplot (three files)
6. R output of linear regressions that form the basis of my statistical interpretations
7. To my surprise, this article was shortlisted for the Kantar Information Is Beautiful Awards