Fun with NFL Stats, Bokeh, and Pandas

&gt; The small spikes at 5 yard increments is interesting and I don&#x27;t really have a good explanation other than to think that whoever recorded the yardage data liked rounding to the nearest 5 if it was close. Anyone else have any other ideas?<p>I&#x27;ll go with the idea that the refs are biased with their ball placement and tend to put the ball on lines [0]. Also, players&#x2F;teams practice, speak, think in 5 yard increments, so that&#x27;s got to bias things a bit. &quot;We&#x27;ve got to get to the 35 yard line for Morten to have a chance.&quot;<p>[0] <a href="https:&#x2F;&#x2F;gutterstats.wordpress.com&#x2F;2015&#x2F;11&#x2F;03&#x2F;are-nfl-officials-biased-with-their-ball-placement&#x2F;" rel="nofollow">https:&#x2F;&#x2F;gutterstats.wordpress.com&#x2F;2015&#x2F;11&#x2F;03&#x2F;are-nfl-officia...</a>

As someone who&#x27;s done a lot of numerical analysis and watches a lot of football, the analysis here is pretty rudimentary.<p>&gt; On third down, pass attempts outnumber run attempts at almost a 4 to 1 clip. This is likely out of increased desperation to make a first down.<p>Running is a low variance low yardage option, passing is a high variance high yardage option. Passing on third and medium to long is an obvious dominant strategy. Pulling the goaltender in hockey or bringing the keeper forward in soccer when trailing late in games&#x2F;matches serves much the same effect.

Does anyone have any insight about what kind of jobs are out there for people with the kind of skills demonstrated in this post?<p>I have a lot of data exploring, cleaning and visualizing skills, python&#x2F;SQL skills and experience using it to make business decisions, but this type of thing falls short of what most people would consider &quot;data science&quot;

I feel like this is a waste of bokeh&#x27;s potential and matplotlib would have sufficed. I love bokeh for the interactive capability and controlling what you plot, zooming, and just overall more immersive feeling than a static 2d plot.<p>Still very interesting and insightful and lovely plots generated.

I&#x27;ve always enjoyed this project for pulling nfl stats. <a href="https:&#x2F;&#x2F;github.com&#x2F;BurntSushi&#x2F;nflgame" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;BurntSushi&#x2F;nflgame</a>

For a while I had a process to extract the top ten highest-WPA plays from my favorite team&#x27;s (Broncos) most recent game. But then my data source dried up. I&#x27;m glad to find out about nflscrapR, it seems like that I might be able to figure out how to do that report again with recent play-by-play data.<p>Incidentally, that report was <i>really</i> fun during the 2011 Broncos season. Normally when you are finding the plays with the largest WPA swings, you&#x27;d expect them to be distributed among both teams. But since Tim Tebow became starting quarterback, I searched for the top ten largest WPA swings for the rest of the season - and from what I recall, every single one of those dramatic plays was in the Broncos favor. Weird. :-)

&gt; Passing becomes more and more popular as you use up your downs.<p>Just in case author sees this, this is wrong, right? It should read Running becomes more and more popular as you use up your downs? (Regarding <a href="https:&#x2F;&#x2F;j253.github.io&#x2F;blog&#x2F;images&#x2F;article_01&#x2F;01_play_by_down.png" rel="nofollow">https:&#x2F;&#x2F;j253.github.io&#x2F;blog&#x2F;images&#x2F;article_01&#x2F;01_play_by_dow...</a>)

Very cool! I would love to see heatmaps of play type with downs on one axis and yards-to-first-down on the other.

this is super cool! well done.