The 2014 World Cup has been an amazing experience. It will enter history as the World Cup where Brazil collapsed in front of their home crowd, where the world fell in love with a fresh and talented Colombian side, and where three-at-the-back defenses proved that they’re back from the dead. But it was also the World Cup where the world at large tasted the use of stats in football, and seemed to like it.
Over the past years, the small community of stat loving football bloggers have been cooking some nice concepts that proved tasteful to some, and at least digestable to most fans. The concept of Expected Goals is the best example, and it is now more accepted than ever. Intuitively, separating poor from good quality chances makes a lot of sense, and ExpG allows us to communicate much better than simple shot counts.
This post will aim to do just that: communicate different aspects of shooting behavior. In one plot, I hope to separate quality shooters from quantity shooters, involved shooters from uninvolved shooters, and efficient from inefficient shooters. That’s quite a lot, and it runs the risk that every data visualization carries: showing too much in one picture. Still, on this one, I’m convinced, dear reader, that you can do it.
So, here’s the plot I was talking about… And before going into further details, I should point you to Stephen McCarthy’s inspirational work on data visuals, which has obviously formed the inspiration for this design.
Nice colors, right? For a full size version, click on it.
This plot combines four elements that constitute a player’s finishing. The horizontal axis is simply the number of shots per 90 minutes played, and the vertical axis is the total amount of Expected Goals per 90 minutes. Both dotted lines represent the two standard deviations mark.
Of course, for all information in this chart, penalties are excluded. Oh, and only players playing over 30% over minutes available are included to prevent the per 90’s from being screwed.
The nice rainbow of colors represents the average ExpG per shot, ranging from very poor (red), through average (green), to excellent (purple / pink). Since ExpG per shot is the same as dividing the vertical axis by the horizontal axis, the colors are nicely arranged in the chart. Poor shot quality will prevent a player from building up ExpG, so red and orange dots will fly at the bottom of the balloons, while high shot quality helps build up ExpG quickly, and leads to the pink/purple/blue dots flying on top.
The fourth parameter is the size of the dots, where bigger dots represent more goals scored. Players with bigger dots than those around them, like Alfred Finnbogason, have converted at a more efficient (and probably unsustainable rate) than others. Reversely, players with relatively small dots, like Mulenga, Havenaar and Depay, have converted inefficiently, which, by the same line of thought, is expected not to carry over to future performances.
Memphis Depay has been the absolute shot monster of the 2013/14 Eredivisie, but with his limited shot quality, he remains quite a distance behind the most dangerous strikers of the league: Graziano Pellè and Jacob Mulenga. New Ajax signing Richairo Zivkovic already completes the top three of most dangerous strikers at 17 years of age, with both a high shots count and high shot quality.
Hakim Ziyech and Oussama Tannane have a shooting discipline problem. Both rank in the top six in shot frequency, but also in the bottom of the league with respect to shot quality.
In the end
This chart conveys a lot of information at a single glance, and provides even more for those patient enough to spend some more time on it. In the near future, you will find similar graphs on my twitter timeline, which I’m using more and more to pop out visuals, when I can’t find time for a full blog post or when I don’t want to repeat myself just with updated numbers. If you’re interested in this blog, you may want to pick up the visuals there too.
Once more, this post is inspired by the great visuals of Stephen McCarthy. Follow him!