The Race to Alaska is a self-supported engineless boat race that runs the treacherous, isolated, and beautiful waters of British Columbia’s Inside Passage–750 miles from Port Townsend, Washington to Ketchikan, Alaska. I had the privilege of participating in the race this year as one half of Team Excellent Adventure in a 17-foot sailboat that we rigged up with oarlocks and a sliding rowing seat so we could row when the wind was light. We completed the ordeal in 16 days.
Every boat in the race was fitted with a SPOT satellite tracker that recorded speed, heading, and position every fifteen minutes. I decided to do some quick visualizations of that data to learn a bit more about our experience and the experience of the other similar and dissimilar boats that we were sailing against.
The boat selection icons are scaled to appropriately match the relative lengths of the different boats. The first visual shows straight-line distance from Victoria for each of the SPOT check-ins. Horizontal lines represent stopped boats, and the steeper the line the faster the boat was moving. Turn on MAD Dog Racing to see what a fast boat looks like. Turn on Excellent Adventure and Bunny Whaler to see a pair of boats that were pretty evenly matched–both 17-foot monohulls. After a bit of back and forth early in the race we traveled together 7/1 through 7/3 along Johnstone Strait where strong tidal currents prevent forward progress every six hours–you can see the points where we anchored and waited for the tide to change. (A)
After a pause in Telegraph Cove, Excellent Adventure continued on with only 3 stops before hitting Ketchikan (thank God for having a small bunk on the boat!). One of the reasons we stopped was strong weather (B) and you can see that it affected Bunny Whaler a bit later on. Finally you can see Bunny Whaler put the pedal to the metal and scream into Ketchikan with a killer last day. (C)
The rest of the visuals are pretty self-explanatory… I might put together a post later on explaining how I built the reports and transformed a stack of lat/lon/timestamp data into the visuals you see here. And because Power BI is pretty neat, I only need to change a single line of code to point my queries at any other race event in the database (R2AK 2015, R2AK 2017(!), Swiftsure, etc) to have this same set of interactive reports filled with the new set of data. Woo!
Let me know what you think! Big thanks to Northwest Maritime Center for putting on an awesome event! I can’t wait for next year!
11 thoughts on “R2AK”
Amazing work! Well done. I love learning about Power BI.
reminds me of discovering Pivot Tables in Excel back in the mid ’90s
Where is Angus on Pg 5/6?
Thanks! It looks like the data source is missing LOA for some boats (Angus, ALULA, Broderna, LITEBOAT, and Uncruise) so that means they don’t show up on the graph. I’ll work on adding those in in the next day or so!
Great, thanks a lot!
All boats should now show up in the scatter plots. I also switched the legend so it shows boat type (mono/cat/tri) instead of finishing status, which I think is a more interesting thing to look at.
Great work and I like the changes.
One thing, the pop-up screen on each boat on the scatter plots (4 of 6) shows “MAX OF LAST 24 HOURS AVG SPEED”. Should this read better as “BEST 24 HOUR AVG SPEED”?
Great work, thank you!
Now, could you please also publish a version with the 2015 data?
Well done! Using data visualizations to share the amazing R2K experience is very cool. It adds so much context to the varied boats and experiences had by each crew. Very cool.
Sweet use of Power BI embed! An ancient discipline meets a very modern one.
Bravo, Ben! I hadn’t heard of Power BI and am impressed.
Hey, can you publish the 2016 data, or send them to me so I can archive? The 2015 data are available at http://searunners.net/data and I’m happy to put a copy of the 2016 data there as well.
Also, there’s some LOA/etc metadata and inter-annual stats that might be of interest here —
Scott in Seattle
Team Searunners 2015
Also, it would be a serious analytic challenge but very interesting to blend in wind and tide data from along the race course. I’d be particularly curious about any wind-less periods when we could get some data on human-powered performance.
In 2015, the first few hours of the Race were windless which presented an opportunity to get some mean speeds. This is my first effort to compare the human-powered solutions in that first year — http://www.mattjohnsonboats.com/2016/06/05/human-powered-boat-speeds-at-the-start-of-the-2015-race-to-alaska/
Could Power BI coax out some additional comparisons?
Thanks for the feedback! I really wanted to include wind data as well but unfortunately the number of wind observation stations up the inside passage is…very low. It would be a fun project to try to pull in interpolated/predicted wind data from SailFlow or some other service. Ultimately though, our experience was that the wind we observed on the water in the vicinity of observation posts was only occasionally close to the published observations. And it very seldom matched the interpolated/predicted wind. For us, too, the difference between 0 and 5 knots of breeze was the difference between rowing and sailing–I’m not sure how much resolution the wind predictions have.
Tides though… that’s more promising. And could be relatively easy to include. You’d just need to build up a grid of regions with known tidal currents and create a table in the model for region/current/timestamp and make a lookup for lat/lon to region. Interesting idea…