Category Archives: Data Viz

Google Trends, R, and the NFL

A week or so ago I saw a tweet related how the NFL lockout was affecting the search traffic for “fantasy football” on Google (using Google Trends).  Basically, the search traffic (properly normalized on Google Trends) was down prior to the end of the lockout.  I decided to explore a bit with this myself and chose to look into the RGoogleTrends package for R.  The RGoogleTrends package is basically a convenient way to interact with the Google Trends API using R.  Very cool stuff.  All of my code can be found at my GoogleTrends repo on github.

My first query was to pull the google trends data for the past seven or so years using the search term “fantasy football”.  The following figure shows the results over time.  It’s immediately obvious that the normalized search scores for “fantasy football” were on the decline (2011 over previous years) prior to the end of the lockout; however, it appears that interest has since recovered.

I then decided to look at the trends for “NFL”.  There isn’t a dramatic decrease in the (normalized) searches for “NFL” prior to the lockout’s end, but you do see a huge spike in searches after the announcement.

A few notes:

  • It would be interesting to align the curves in the last plot by the day of week.  That is, it would be nice to compare the trends scores, as an example, for the 7th Wednesday prior to the start of the NFL preseason or something.
  • In order to use the RGoogleTrends package, you can use the cookies in Firefox to pass username and password if you just log into gmail (or another google service).


Filed under Data Viz, ggplot2, R, Sports

The RDSTK Presentation at Denver R Users Group

Last night I presented a talk at the DRUG introducing the R wrapper for the Data Science Toolkit.  Lots of good questions, good forking, and good beer afterwards at Freshcraft.  The slides are given below.

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Filed under Data Science, Data Viz, R, Web 2.0

A Rule Change in Major League Soccer?

I have to admit that working with my Major League Soccer data set has been slow going.  There are a few reasons:  (1) I have a full-time job at the National Renewable Energy Lab and (2) the data isn’t quite as “rich” as I initially thought.  As an example, the MLS site doesn’t list the wins and losses for each team by year.  That seems to be a fundamental piece of “sports”-type data, right?  In any case, I did come across something that I can’t seem to answer.  If you know somebody that works with MLS, send ’em my email address and tell them that I want answers, damnit!

So following up on my previous MLS-related post, I wanted to see if I could pinpoint why goals per game has been decreasing in recent years.  My first thought was that with MLS expanding, more US-based players transferring overseas, etc., that the overall talent level in MLS has suffered a bit in the more recent years.  One way that this might manifest itself in the data is by having less shots “on target” or “on goal”.  Therefore, I looked at the number of shots on goal vs the number of shots and also vs the number of goals over the years.  The two figures are given next.

Based on the first figure, one could argue that the shooters are becoming a little less accurate.  That is, the number of shots on target per shot has decreased by about 10% over the course of the league’s lifetime.  Shots on goal per goal seems relatively steady over this same time period.  This might suggest that the league’s strikers are getting slightly worse whereas the quality of the keepers is holding steady.  That, of course, could contribute to the decline of goals per game.

I also decided to look at the number of assists per goal.  Why?  Well, my logic is that if there are more assists per goal, then there might be better overall team play.  Conversely, a decrease in this number might be a result of teams having one or more stars (hence, more individual brilliance) and less of the quality, build-up-type goals.  Make sense?  C’mon, I’m trying here!  Anyway, here is the resulting graph.

Whoa, what in the hell happened there?  The data look a bit suspicious.  Specifically, there seems to be a serious change between the 2002 and 2003 seasons.  So I made a similar graph, but I separated by the different time periods.  Here ya go.

What does this mean?  My hypothesis is that there was a fundamental change to the rules in how assists were recorded between the 2002 and 2003 seasons.  Unfortunately, I can’t confirm this.  I’ve searched the web, read the MLS Wikipedia page, read a good amount of the MLS website, and can’t seem to find anything related to a rules change that might result in this sort of phenomenon.  Sooooo, if you have any ideas, send ’em my way!

This will likely be the last MLS-specific post for a while.  Unless I can find some more data, I’m giving up — their data is just not that interesting.  Notice that I didn’t say that this would be my last soccer post.  Hopefully I can scrape some EPL (England’s Premiership) data.  Given that their league has been around for more than 15 years, it should be a bit more interesting than mine.

If you’re interested in taking a look at the data and/or code yourself, I’ve created a github repository for your perusal.  Feel free to pass along your comments and/or questions regarding any code — I have thick skin.

So what’s next?  I am thinking about comparing my current workflow of (a) scrape with Python and (b) analyze with R to just doing everything in R (e.g., using the xml package).  Hopefully, I can post some time comparisons soon!

Addendum:  According to at least one blogger, the recording of “secondary assists” was changed after the 2002 season.  I’m not sure why they record secondary assists in the first place — I guess MLS wanted to appeal to the hockey people in the early years.  Here is the bloggers take on secondary assists:


Filed under Data Viz, ggplot2, R, Sports

Goals per Game in MLS

I promised something related to Major League Soccer and here it is.  Caveat:  It’s not much.  Why so sparse?  (1) The data is a bit messy due to teams folding, expansion, name changes, etc.  (2)  I was backpacking all weekend and didn’t have time to work on this side project.  Yes, I have a real job and working during the work week is a bit difficult.

My first step was to scrape the “stats” section of the MLS site to get all of their public data.  Or at least all of the data that is relatively easy to find and easy to scrape.  I’ll post the code soon once I setup the master repository on github.  Needless to say, I think it looks a bit better than my initial foray into beautifulsoup as posted here.

I decided to look at goals per game by team and year.  Most people who like soccer like goals, so this seems like a good starting point.  Here is the initial figure.

As you can see, there are a lot of blank spaces.  The reason for this is because a lot of teams changed their name and/or relocated (e.g., San Jose), some teams folded (e.g., Tampa Bay), and MLS added teams over the years (e.g., Chivas USA).  The bottom line is that it makes for an ugly graph.  In an attempt to clean it up a bit, I tried to consolidate some of the names.  Here is the new figure.

It still doesn’t look great, but I do think that you can learn a bit from this figure.  Overall, I would say that the goals per game for each team is decreasing over time.  Is it a statistically significant decline?  I dunno.  I’m not writing a paper here — it’s a freaking blog, i.e., speculation reigns supreme!  In any case, this raises more questions.  For example,

  1. Does this apparent decline affect attendance numbers?
  2. What is the cause of this decline?  Better defenders coming into the league?  Um, I doubt it.  I would imagine that quality strikers are being added at about the same rate.

I would hypothesize that it’s just that the quality of the league has improved significantly over the years.  Hence, the teams are holding possession more and not just firing shots whenever they get a chance.  As a result, I will look into attendance numbers, shots, shots on goal, etc. in the upcoming days or possibly weeks.  I believe that some interesting questions can be answered with these data.  However, I am still trying to discover what these questions might be.  If you have any ideas for questions, let me hear about them in the comments section.

The R code for this project isn’t too interesting, so I won’t post it below — it will be on the github repository in time though.  One thing that I did learn about R is that reading in numeric data measured in the thousands (e.g., attendance figures) can be problematic if the numbers have commas.  It took me a while to find the workaround and it’s given below.

mls.reg.dat$h_tot <- as.numeric(gsub(",", "", mls.reg.dat$h_tot))
mls.reg.dat$h_avg <- as.numeric(gsub(",", "", mls.reg.dat$h_avg))
mls.reg.dat$a_tot <- as.numeric(gsub(",", "", mls.reg.dat$a_tot))
mls.reg.dat$a_avg <- as.numeric(gsub(",", "", mls.reg.dat$a_avg))


Filed under Data Viz, ggplot2, R