The Spreadsheet Offense: Analysing historical Fantasy Football data

[Typical disclaimer: I’m British and I just like making graphs, I don’t know as much about NFL as my wild assertions might imply. I’ve played fantasy football for one year now and I nearly got beat by someone who drafted Aaron Rodgers and all kickers, so take this advice with a large helping of salt]

It is with a heavy heart that I am about to reveal the basis of my fantasy draft strategy to the 13 other members of the Edinburgh nerds fantasy football league. My squad ‘THE LEGION OF BABY BOOM’ had a troubled season last year, as I picked Eddie Lacy with the second pick of the draft as he dropped from 230 points on the 2014 season to 120 points in 2015. I also held out until the later rounds to take a Quarterback, picking Sam Bradford and Teddy Bridgewater in successive rounds. I actually remember taking Teddy and seeing pick after pick not taking Bradford thinking “God what losers, I’m going to get both of them! #1, let’s go boomers!”. Subsequently I had a circus show at Quarterback, starting at points Josh McCown, Brian Hoyer etc. If you don’t have context to anything I’ve said above and I’m just naming random millionaires then let it be known that every name I said above played as if they were deliberately trying to disappoint me. I was not the victor of “VONTASY MILLERBALL”.

Anyway, the 2015 season was a clear sign to me that I am not a great NFL scout. Going on pure feeling again is going to get me embarrassed, especially since I spend far too long in a day reading about NFL to lose so badly again. So I decided to use what I have, a huge dataset of NFL players and a love of scattergraphs and histograms to try and override my awful instincts on draft day.

What I’ve got: The fantasy record of every player playing in the NFL from 2000-2015
What I’m going to do with it: Dump a load of graphs which attempt to make the readers of this blog win their fantasy league*, GUARANTEED**
*Assuming NFL.com Classic scoring
**The attempt is guaranteed, nothing else

2008_Draft

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Crappy friends at crappy burger joints: A statistical analysis of 10 million meals

 

[NOTE: I wrote this blogpost ages ago to pitch to another website, for whatever reason it fell through but I feel the need to point out a couple of things:
1. Since writing this, it turns out that Byron is a really nasty company, so if you take anything from this it is DO NOT BUY FROM BYRON, the burgers ain’t that good anyway. As a result I have replaced all use of the word byron with CRAPPY BURGER JOINT.
2. Since I was expecting it to be on another site, the style of it is a bit more sweary, probably just a one off.
3. My friends aren’t crappy and actually I don’t know anyone who does this so don’t think this is aimed at y’all.]

It is 2016 and we still have major issues dealing with the restaurant bill. Too many times you have 10 people sat around a table in Zizzi who each either have to rationalise that “£20≈£18.95 with a tip right?” or sit there for several excruciating minutes waiting for the card machine to go around each person while the dad from the next family up angrily catches your eye from the “Please wait to be seated” sign. Then, in this time crisis enforced upon you by the social pressure of being in eyesight of ‘the sign’, you have a major decision to make: either try and relearn how to use your calculator app to work out how much your meal was or split the bill evenly. What I’m here to show you is that because of this option, its very easy for your crappy friends to take your money.

What I’ve got: The ability to simulate random meals drawn from the CRAPPY BURGER JOINT menu
What I’m going to do with it: Prove that having a bad friend can cost you money

Pay_Bills_Here

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