I’m back to give some more uninformed picks! I’m currently in my office trying to get my code to recognise the large scale structure of the universe (which is easier than it sounds, but I’m finding it harder than it probably is). So I don’t quite have the time to go over last weeks picks. They seemed to do alright, my only worry was that my desire for the model to work was making me support teams I didn’t like in the hopes that the status quo was preserved. TeBOW has turned me into a monster.
This week I have added in the capacity for the model to simulate the rest of the season, which means that I can start to give percentage chances for teams to get to the playoffs. Very literally I am coding these features minutes before I put them up here so if something weird happens then blame me, but also include a bit of pity in your scorn. I had to get this out before NO/CAR! The battle of the “should be in playoffs but pretty unlucky”
Anyway, enough of the foreshadowing, lets go for the 1000th power rankings you’ve read this week!
America, statisticians and the world at large have had a pretty crappy week. What better week then to introduce my overly simplistic statistical model to attempt to predict the outcome of American Football games, TeBOW!
TrueSkill (extended) Based On Wins.
The model takes only the outcome of games that have happened and manages to calculate the rating and consistency of a given team. This allows us to do two things, firstly we can power rank the teams based on their games so far and also we can make predictions about the future games that are going to happen. Every week until the end of the season I will publish the power rankings on a Monday, and then the predictions on a Thursday.
TeBOW is so-called as not only is Tim Tebow a meme and I’m addicted to those page views, but also the model completely ignores any potentially relevant information about the performance of the team, pass yardage, interceptions, etc. All TeBOW cares about is wins no matter what, and I think this is fair to his legacy.
[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
I do quite a few projects which get a few cool graphs in them but no interesting conclusions or discoveries, and so instead of just leaving them to rot in my ‘odd_projects’ folder, I thought I’d start publishing some short posts outlining what i did (like really just an outline, I probably wont go very deep into the theory) and sharing the graphs, so here goes:
What I have: The MNIST database, a database of 70,000 handwritten digits labelled by what number they’re meant to be
What I’m going to do with it: Use principal component analysis to compare relative difficulties of classifying handwritten digits
My gut instinct tells me that NFL running backs are some of the most poorly treated athletes in the world of sport. The rules against hurting running backs are significantly less strict than those for wide receivers or quarterbacks which leads to a significantly larger amount of career ending injuries. Teams know the fragility of running backs and are less inclined to offer them guaranteed money on their contract (money which will be given even in the case that a player cannot keep playing due to an injury) which significantly lowers the career earnings of an unlucky running back. Further to that, coaches treat running backs as expendable due to the simplicity of their task and will often drop an injured one for a healthier model, which due to the pyramid scheme nature of the NFL there will always be. Given enough data and enough time I would like to prove all of the above is true.
However for this post, I want to show that a running back’s age affects their ability to play in the league. Not only that as a running back gets older they are less likely to get a job, but simply being on the wrong side of 30 will dramatically reduce their chance of having a job.
What I have: A database of all players currently active in the league, and a historic database of all drafted players.
What I’m going to do with it: See that running backs over 30 are disproportionately cut from NFL rosters compared to other skill positions.
Andy Dalton is actually very good this season! Johnny Football is actually going to play football! A divisional matchup that the NFL thought would be less one-sided at the start of the season when they decided the TNF games!
Amongst all these heart wrenching story-lines weren’t you desperate for someone to post some simple (and some less simple) graphs to clear the air? Well look no further.
Today I’m starting up the first of hopefully many On Any Given Axes features, where I take a game that I’m watching and share graphs that I’ve made about it. I’ll share the graphs on twitter and copy the tweets here, and will try to respond to any interesting comments on either, so do keep in touch!
What I’ve got: A divisional matchup with two maverick quarterbacks
What I’m going to do with it: Watch it and graph it.