2016 Matchup Guide, Part 5: My Picks

Much of the data analysis at this site highlights how the selection committee seeding process is flawed, and you can use that knowledge to your advantage in making picks. That doesn’t mean you should favor the most probable paths for every section of your bracket. Leave the chalk-filled conservative brackets … Read the rest

2016 Matchup Guide, Part 4: Tableau

Data itself is rarely the story, and a dashboard is only as useful as the questions it can answer. With that in mind, I try to start any data visualization by defining those key research questions. In my professional career, this process usually involves conversations with business users about problems … Read the rest

2016 Matchup Guide, Part 3: Alteryx

Alteryx was a new software for me coming into this project. I’ve typically done my data preparation work using some combination of SQL, Python, and Excel just depending on my needs. I’d seen Alteryx demoed at conferences, though, and was looking for an excuse to try it out. Big thanks … Read the rest

2016 Matchup Guide, Part 2: Python

I’ve been using python for a couple of years now, primarily as a data scraping tool. I’ve uploaded the code I used for the 64to1 viz on github. To run these, you’ll need to install Python plus a few small plugins like BeautifulSoup, Requests, CSV, and Re (runs regular … Read the rest

2016 Matchup Guide, Part 1: The Numbers

This is my third year building a matchup guide for the NCAA tournament. In this post I’ll take you through the numbers in my analysis, and later I’ll go through the process for scraping, shaping, and visualizing our college basketball data set.

There are many places you can go for … Read the rest

64to1 bracket

As I did last year, I’ve posted my 64to1-bracket. Like anyone’s, it’s a lottery ticket that’ll probably come up short, but here’s my thinking on some of the picks:

Kentucky wins it all

Most years I’d advocate for picking a team that isn’t the favorite due to the … Read the rest

Picking Upsets in the Round of 64

When we talk of coin flips, we generally make an assumption of approximately equal probability between heads or tails, i.e., a “fair coin.” Tournament picks are also coin flips, but they’re unfair coins, typically loaded in favor of the stronger seed to varying degrees. The NCAA seeding gives us one … Read the rest

The case for and against Kentucky

One of my favorite March Madness data sources is the pick frequency table (“who picked whom“) ESPN publishes at their bracket challenge site. It allows you to assess where herd mentalities are forming, and you can adjust your bracket accordingly. Your opponents in bracket challenges are other humans, … Read the rest

2015 Relaunch

NCAA Basketball is as seasonal as Christmas. Americans notice it’s going on two months before the big event but mostly ignore it. Then the bracket drops in March, and people are interested until the end of the first weekend when most brackets are busted. The pattern probably looks a little … Read the rest