Football Statistics: the Impact of Smiling

If you've ever watched a professional football game (and this is probably true for most professional sports) then you have seen these little portraits of players that appear at the bottom of the screen. On some TV networks they are actually short video clips where the players announce their alma mater, on some networks they are animations where the players each raise their heads and occasionally blink (these creep me out), and for other networks these head-shots are just still photos. Some players smile in their photos, some do not.

Key & Peele have a recurring bit about this player introduction phenomenon.

While watching a Seahawks game this past year, my mother in law posed an amusing question: Do players who smile in their photos play better football?

The question is simple and whimsical, in other words perfect. I don't know anything about how often these photos are taken, what the player's mindset is when they're shot, or if there is any prior expectation about attitude/persona and player record. I set out to find some answers...

For this study I am only focusing on Quarter Backs (QBs) in American professional footbal (NFL), though it would be easy to extend to all positions if anyone can help me get the data! Right away I know I'll need a few ingredients: photos of each player, some classification of their smiles, and some real stats on their records in the NFL.

Random Forest for Time Series Forecasting

I recently spent a week at the 2014 Astro Hack Week, a week-long summer school + hack event full of astronomers (and some brave others). The week was full of high level chats about statistics, data analysis, coffee, and astrophysics. There was a great crowd of people, many of whom you can (and should) follow on Twitter. Below is a quick post I wrote up detailing one of my afternoon "hack projects", which was originally posted on the HackWeek's blog here.

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After Josh Bloom's wonderful lecture on Random Forest regression I was excited to try out his example code on my Kepler data. Josh explained regression with machine learning as taking many data points with a variety of features/atributes, and using relationships between these features to predict some other parameter. He explained that the Random Forest algorithm works by constructing many decision trees, which are used to construct the final prediction.

I wondered: could I use the Random Forest (RF) to do time series forecasting? Of course, as Jake noted, RF only predicts single properties. As a result, RF isn't a good choice for doing trend forecasting over long time periods. (well, maybe) Instead, this would use RF to just predict the next datapoint.