Visualizing NFL Data

Kevin Feasel

2016-08-31

R

Allison Tharp looks at NFL play-by-play data using R:

Lets look at how teams played on offense depending on where they were on the field (their yardline) and the down they were on.  The fields in our dataframe that we will care about here are yfog (yards from own goal), type (rush or pass), dwn (current down number: 1,2,3, or 4).  We will want a table with each of these columns as well as a sum column.  That way, we can see how many times a pass attempt was done on the 4th down when a team was X yards from their own goal.

To do this, we will use a package called plyr.  The Internet says that this package makes it easy for us to split data, mess with it, and then put it back together.  I am not convinced the tool is easy, but I haven’t spent too much time with it.

Check it out for some ideas on what you can do with R.

Related Posts

The Theory Behind cdata

John Mount has a video explaining the concepts behind cdata: We also have two really nifty articles on the theory and methods: Fluid data reshaping with cdata Coordinatized Data: A Fluid Data Specification Please give it a try! Click through for the video, which I found very helpful in tying together a number of data […]

Read More

Microsoft R Open 3.4.3

David Smith announces Microsoft R Open 3.4.3: Microsoft R Open (MRO), Microsoft’s enhanced distribution of open source R, has been upgraded to version 3.4.3 and is now available for download for Windows, Mac, and Linux. This update upgrades the R language engine to the latest R (version 3.4.3) and updates the bundled packages (specifically: checkpoint, curl, doParallel, foreach, and iterators) to new versions. MRO is 100% compatible with […]

Read More

Categories

August 2016
MTWTFSS
« Jul Sep »
1234567
891011121314
15161718192021
22232425262728
293031