I have been experimenting with some different positional visualization ideas and this is hopefully the first of a handful of related posts. Once I stop being technically inept (and/or lazy), and figure out how to properly plug MySQL into Java/Processing, I can mass produce these for every player in the league. I picked Dempsey because he played very regularly for Fulham (35 starts) last season and was relatively integral to their success.
What you’re looking at is the average position of Clint Dempsey during the 37 EPL games that he appeared in for Fulham during the 2010-2011 season. Light green circles represent his position when Fulham won, the dark green circles represent when Fulham drew, and the red circles are when Fulham lost.
All circles are connected to the season’s average position via a line to show how different the position was from the “norm”. The concentric opaque circles represent one and two standard deviations from the average.
We can make a couple interesting observations from this visualization. First is the obvious tenancy for Dempsey to drift forward during Fulham wins.
Astute readers will point out the Dempsey was deployed as both a Striker and an outside winger during the season. I recognize this, but it’s tough to discount that Fulham didn’t lose in the 10 games where Dempsey was deployed furthest up the pitch. I recognize that this correlation does not necessarily imply causation. A player shifting backwards could be caused by his team losing – not the cause for the team losing.
The other interesting observation is that the further away Clint’s average position is, the more likely Fulham is to win. For positions beyond one standard deviation, Fulham seems to be about three times as likely to win.
Further ideas for this kind of visualization is including some extra dimensionality. For example, if I weight the size of each circle based upon the positional standard deviation during that game, it would add some meaningful context to some of the outlying data points.