How I Found Amazing Sugar Bowl Tickets

Getting these tickets was no accident. As soon as it was announced that Ohio State would be playing Alabama in the Sugar Bowl, I knew I had to go to New Orleans. I didn’t plan on sitting in the nosebleeds either. No, this type of game deserved these type of seats. How did I find them you ask? Surely I must have spent a fortune. Think again, my friend! Instead, I let the computers do their magic for me.

The ticket marketplace is pretty opaque, and finding a good deal can feel like guesswork. Thankfully, SeatGeek is a service that aggregates the available tickets for all kinds of events, and also applies some fancy logic to tell you which tickets are actually good deals. In a short time window, their logic is often all the information I need to make a ticket buying decision.

However, the stakes were higher for the Sugar Bowl and I had a large window of time to make a buying decision. By collecting ticket data over time, I could see if there were any trends that I include in my decision making process. I collected data from SeatGeek for almost a month and visualized it a few different ways using the Highcharts javascript library.

I visualized this data in two ways. The first is illustrated below. The graphic shows how the average price moved over time and in relation to the volume of tickets available. Pretty clearly we can see when new tickets entered the marketplace, as well as a pattern of drastic decreased availability in the days closest to the game.

Screen Shot 2015-06-05 at 10.41.12 AM

The second visualization is a scatter plot showing a snapshot of all of the tickets available.


Hacker News “Who’s Hiring” Trends

Hacker News is somewhat of a bellwether for the tech industry in many ways.  The discussions that take place in that community are strong signals as to what trends are appearing in Silicon Valley and elsewhere.

On the first day of any given month the top post is guaranteed to be the “Who’s Hiring” post with at least 200 comments and often over 400. I was curious if the content in these particular threads would surface any trends or insights, so I built a tool that allows you to view the word frequency over the past 12 months of Who’s Hiring threads.

I used the Algolia API to download the data and then created two sets of json files, one with the number single word occurrence, and another with two word string occurrences (so you can compare “San Francisco” to “London”).

To better compare the trends month over month I’ve taken a look at the number of occurrences divided by the number of words for that month. To get a view of the pure word count just uncheck the ratio box.

Here are the trends that I was most curious to investigate:


Screen Shot 2014-07-22 at 6.24.09 PM


Screen Shot 2014-07-22 at 6.24.25 PM

Android v. iOS

Screen Shot 2014-07-22 at 6.24.41 PM

Try it out yourself! I might suggest “growth hacker” and “data science”

Here’s the Github repository

Share any cool comparisons on this HN post

D3 Map

When I started with D3 I wanted to make a visualization that displayed data by state. I had a lot of trouble (which ended up being due to a corrupted us-states.json file). I thought I would share this to help anyone else looking to make a map using D3js.

var w = 960;
var h = 620;

var projection = d3.geo.albersUsa()
.translate([w/2, h/2])

var path = d3.geo.path()

var svg ="body")
.attr("width", w)
.attr("height", h);

d3.json('us-states.json', function(collection) {
.attr("d", path)
.attr("class", "state")
.attr("id", function(d){return})
.style("stroke", "white")
.style("stroke-width", 1.5)
.style("fill", function(d,i) {
return "rgb(0,0," + (i * 4) + ")"