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?

On Football has quickly become one of my favorite blogs to follow (Twitter) and one of the most interesting posts of late was an analysis of “finishing” in Major League Soccer.  OnFooty analyzed conversation rate vs. the number of shots for each team over the total season.  Typically conversion rate is calculated as goals/shots on target, but OnFooty has a terrific case for using shots in lieu of shots on target.

In conclusion, Sarah said “The upper right quadrant (teams that are above average at both creating and finishing) contains the MLS Cup Finalists and the number one seeds for each conference.

This quickly led me to the Premier League and interestingly enough, the same analysis holds true through 30 rounds (29 for some teams) this season.  The top four teams are all in the upper-right quadrant. 

If you follow English football, you might note these things as well:

1. Tottenham needs to improve their finishing if they want to secure a returns spot into the Champions League qualification round (for the 4th place finisher)
2. Blackpool (one of the most exciting teams to watch) score at a terrific rate, but since they’re in 15th place in the table, they obviously need to work on their defense (their philosophy is to outscore their opponents since they know they can’t stop them
3. Wigan has the worst conversion rate and sits last in the table…enough said

Play with the view below.  Choose your vertical and horizontal axis, check out the different tabs.  What do you see?

I’m compelled to look historically to see if this theory holds true season after season…maybe one day when I have more time on my hands (I’m busy tallying stats for my daughter’s soccer team).