As I’ve done every Monday this year, this morning I ran my scripts that gather data about the potential Democratic presidential nominees.
I’ve crunched those numbers, and run them through a bit of machine learning magic, and generated predictions. The predictions are the percentage chance that each significant candidate gets the Democratic Party 2020 Presidential Nomination.
Welcome to the March 2019 version of my quest to provide continual snapshots of who is winning the race to be the Democratic nominee in 2020.
After a suggestion by a reader over on Minds I have switched up the format a little. I am now providing actual percentages of each candidates chances to gain the nomination.
Now that the NFL Scouting Combine hype machine has come to a close, I thought it would be a good time to send out my scrapy agents to gather mock draft opinions from across the web.
I collected a variety of 2019 NFL mock draft lists from dozens of sources, well known and otherwise. I ran this data through some Python magic to create a meta mock draft.
I am super excited to announce the March Madness edition of the 2020 Democratic Candidates Power Rankings!
I’ve invested over 20 hours into my rating system since last month’s version ran. In February I ranked the chances of 2020 potus candidates regardless of party.
This month it is strictly Democrats. The new version of my system is much slicker and contains a couple of totally new metrics.
As my bots continue to bring me data from all across the interwebs I am constantly thinking about interesting questions to ask it.
As I have been focused on numbers generated by various sports leagues I began to wonder about the popularity of these various leagues themselves…
Every morning my bots gather information about news from across the web. I gather meta news if you will…
I do a number of different things with this data, but it all boils down to turning that raw meta data into information. One of the first functions I wrote was: