Ben Hannas sent me data for the four missing valleys in the middle of Stage 9. Those four additional data points modify my Stage 9 prediction:
- Stage 9: 5h 19' 43" (prediction -- four extra data points)
The middle of Stage 9 looks to be an amazing ride!
Hey John. Do you predict winners too or just finishing times?
ReplyDeleteI predict winning times only. Thanks for your interest in my blog!
ReplyDeleteCool! Did you create a model for this? Stage 9 is a tough one to predict in terms of winning riders. I'm playing in the versus fantasy league. I'm thinking a break could get away tomorrow. Do you factor breakaways into your model?
ReplyDeleteGolden Stage Girl: Good questions! I do not factor breakaways into my model. I take data from stage profiles given on the Tour de France website, and then sprinkle in some physics of cycling and human performance. Incorporating breakaways and other strategies are quite tough to incorporate into a model.
ReplyDeleteNeat! Are you an avid rider yourself? I found this article recently that I thought was interesting:
ReplyDeletehttp://home.trainingpeaks.com/articles/cycling/tour-of-california-vs-tour-de-france.aspx
I also work for Expresso, we build the worlds most advanced stationary bikes & we've got lots of good rider data from over the years. There are a couple videos here that will give you an idea how the bike works:
http://www.youtube.com/expressobikes#p/u/6/7ksiQVbZVa4
I wonder if you could apply your formula to our courses. Our physics model on the bike doesn't account for the advantage of riding in a peleton, so whatever you use for time trial stages may be more appropriate.
By the way. I'm actually Ross from the videos. My wife has a blog that she linked to my Google account, which is why my avatar shows up as "Golden State Girl."
ReplyDeleteRoss: Thanks for the links. I'll be sure to check them out! As for riding, I love to ride a bike, but physics keeps me too busy for serious riding.
ReplyDelete