Analytics For The Perfect Race Across America
JeanFrancoisPuget 2700028FGP Visits (14289)
Applying analytics to sports is one of the fun part of my work. I had a great opportunity last year to work as part of an IBM team to help ultra cyclist Dave Haase race across America. Racing across America is quite a challenge: imagine a 3000+ miles, non stop, race across USA, with over 110,000 feet elevation (see pictures below). Cyclists can race as they wish, rest only when they chose to. Last year winner slept about one hour every 24 hours, for 8 days. Dave Haase finished close second, having rest for two hours every 24 hours, for 8 days too. I wouldn't be able to sustain this even without cycling: I need way more sleep per day.
Just to give you a hint on the difficulty of the race, here is the elevation profile along the route, from RAAM site:
and here is the route itself:
It was kind of nuts to be part of this last year, but what is even more crazy is that Dave decided to race again this year. Of course, we decided to help him again. You can find more details on how IBM is helping on this blog. I will focus here on my part.
I basically help Dave's team decide when and where he should rest. The decision is based on road conditions (elevation, slope, etc), Dave's condition (the power he can provide when cycling), and weather conditions, especially wind. For this I had to develop a series of analytics models. We are reusing these models this year, albeit with this year data. Data come from various sources:
We use the following models:
The result is displayed on a dashboard that Dave's team can access anytime.
The race started well for Dave, but he had a major health issue that made him stop for 17 hours. When he resumed racing (yes, he resumed!) he was 10th. As I write this is is back to the second place, which is absolutely incredible. I am proud we are helping him a bit, but he is doing the job, not us.