Surf Mavericks: Update 1

Last Sunday the Mavericks Invitational ran – which means tens of thousands of people pay attention to the break for a day. For most, the invitational and wave are so tightly bound that they don’t realize you can go there on other days.

I decided not to go on Sunday because the idea of watching a surf contest from a parking lot seemed a bit crazy. After a rogue wave washed up the beach a few years ago, new policy it that the beach and bluffs must be closed during the invitational for safety reasons. Policy ruins everything good.

About 16 hours later the wave was still breaking, and access to the beach was back open. I decided to go watch it for a bit.


Like any good surfer, I got there just before sunrise. There was just enough swell that it was breaking, but it was right in front of the rocks so no one was out. This did give me a good chance to get to know what is under the water a bit. I watched the boils and got a good idea of where most of the rocks are – there are a lot more than  I thought.


After a few hours, I drove up north to Ocean Beach, which was a solid double overhead. Wish I was in shape for that.

Shoulder: Working – but still not 100%

Breath Holding: 2:01

Days to Go: 342

Innovation Session: Tony Gonzalez Infographic Part 1

My idea for this week’s session was to look at some stats about Tony Gonzalez.

I originally thought I would be coming up with something like a ‘5 reasons why Tony Gonzalez shouldn’t retire’ post – but after thinking about it I realized that data wasn’t the issue here. Tony doesn’t need convincing that he still has it, or that the Falcons are so close – his decision, whatever it will be, is driven by personal reasons. I respect that and don’t want data to add needless pressure.

So, already knee deep in data about the career of Tony Gonzalez, I decided to honor his career by creating an infographic.

I am about 7 hours in and it is looking sweet – I will likely have to finish it this weekend though – so look for the final product next week.

In the mean time, here is a sneak preview…


Innovation Session: The Most Normal State – Part 1


The idea started as I was driving back from a caving trip in southern Utah. My friend Mark & I drove through Colorad City, Arizona, known for its community of polygamists.

“Wow” we thought “this is not a normal town.”

Like I mentioned, we had just spend a few days in Utah, a state known for its disproportionate percentage of followers of the Mormon faith. And as we drove back we crossed through Nevada, a state that you are always aware when you enter on account of the immediate presence of large casinos on the border. Not exactly normal.

As we pondered what it was that we meant by ‘normal’ we realized that our beloved home of California certainly wouldn’t fit the mold.

“So,” we asked ourselves “which state is the normal one?”

If the states were family members in a bizarre sitcom – which one would be the main character? The relatable one. The one who’s character wasn’t saturated with stereotypes and caricature.

We debated for the better part of a few hours – which is nice on a 12 hour drive. Illinois and South Carolina seemed to come out on top – but none of this was based on anything solid.

So, operating under a belief that there is a state more normal than the others – I am going to attempt to pull together some data that will make clear which one it is.

Wish me luck.

Step 1: What makes a state normal?

As we drove we began to brainstorm a list of ideas of attributes of a state that could help to determine its normalness or lack thereof.

We settled on a few major categories:

  • Politics
  • Economics
  • Social Norms
  • Geography & Climate
  • Cultural
  • Sports

Each category could then be broken down into a number of attributes that are more quantifiable. For example – in politics, we would look at the state’s popular vote breakdown as compared to the nation’s in the most recent presidential election.

The methodology we will take with this project is to assign, for each attribute, an absolute value z-score for each state. This will show which states are closest to the national mean – the ones with low z-scores – and which ones are not – those with higher z-scores.

We realized that it didn’t matter if they were above or below the national average because we will be comparing across attributes that are in no way related. If we tried to keep track of positive and negative values we would be presented with the challenge of determining whether high amounts of rain was more of a Democrat or Republican thing. We don’t care – if you’re not close to the mean – you’re not normal.

Step 2: Getting the data

Now that we know what we know what we are measuring and comparing against, we need some data. I’m really hoping there is a massive state info API out there – but in the likely case that there is not – it might be time to start web scraping & mechanical turking. This is going to be the fun part.

I will be keeping all code publicly viewable on github – so feel free to contribute: Most Normal State Github Repository


You can ready part 2 here, where I attempt to get data from the US Census.