The Facebook Data Science team does some pretty awesome analysis of friend relationships!  On Monday they published an article on fan relationships between NFL teams.  It’s very interesting content.  I was working on the last visualization in the post with their team but we didn’t get it done before they had to publish their post.

So with their permission, I’m publishing my version of NFL fan relationships. Here’s their explanation for how to read it:

Even the most die-hard fans among us have some friends who root against us. While it turns out that most friendships between NFL fans on Facebook are between fans of the same team,  we wondered, what about the rest of the friendships? Which rival teams' fans are most likely to hang out on Sunday to bond over beer and wings despite their conflicting allegiances?

The following viz shows the fan-friendships for each team in the league, excluding friendships between users who like the same team.  Highlight a team by choosing it from the list on the upper-right.  Filter by Division.

If you have a large monitor, check out this version.

There are a couple of interesting findings.  Dallas and Pittsburgh are nearly always in the top 3, while Jacksonville, Houston and Buffalo aren’t very popular.  Perhaps this helps explains why certain teams are on TV more than others, or perhaps they’re more popular because they’re on TV.

Where does your favorite team rank?

The Soccer by the Numbers blog continues to provide me great inspiration. After Manchester City destroyed Manchester United 6-1 on October 23rd (at ManU), they blogged about the results and provided a bit of statistical analysis.  City has long been the “noisy neighbors” to United, but they now are doing their best to buy a title, rather than grow their own talent.  Soccer by the Numbers posed this fundamental question:

We now know that the outcome of the match was truly unusual. But how unusual?

They created the table below, which is a frequency distribution of scored lines since 1907 of matches at Manchester United.  To use the chart you simply identify the score line by going across City’s scores then down ManU’s scores.  So the 6-1 scoreline has occurred 2.99% of the time since 1907.  Clearly this was an unusual result.

But I think this table could be improved.  I made these changes:

1. Changed the numbers to percentages and rounded to one decimal.  Two decimals is unnecessary precision.
2. Removed most of the gridlines so that the lines separate the data from the categories
3. Formatted the results as a heat map.  I chose a red-white two-color scheme since Red is ManU’s color.  This makes the largest percentage of result very obvious.  For example, you can now easily see, without having to scan across all of the data points, that 1-1 is the most common score line…boring result!
4. Formatted the totals as a second heat map.  I chose a brown-white scheme for these.  The totals show you the % of the total goals scored for each time.  ManU has scored one or two goals 64.2% of the time while City has scored one or two goals 58.2% of the time.

Which format do you like best?  Does one make the story easier to interpret than the other?