A Case for Possession – How Goals Change Games

Possession statistics are notoriously misleading. Both Chimu Solutions and Soccer Statistically have found that MLS teams that possess the ball more than their opponent actually win less than 50% of the time. 5 Added Minutes found similar trends in the EPL, suggesting that winning teams only had more possession an unconvincing 50.1% of the time.

All of these posts are fantastic at pointing out the problems with the possession percentage metric and how misleading it can be from a 1,000 foot view. This is surely not a popular viewpoint at a time when the media loves to shove Barcelona’s possession statistics down our throat. Questioning long held beliefs is incredibly healthy for the future of soccer analytics.

However, I think everyone would agree that possession does mean something. It’s the quantifying this something that has proven to be difficult. By slowly crossing out things that this something could possibly be, we will eventually be left with what it has to be.

Let’s begin by thinking of each goal scored during the 2010-2011 EPL season as an individual game. This game’s length is the amount of time between each goal. For example, sticking with this blog’s Fulham theme, let’s look at Fulham’s 2-2 draw with Manchester United at the beginning of last season.

There were 4 “games” in this fixture: the 0 to 10th minute period before Manchester scored, the 10th to 54th minute before Fulham scored, the 54th to 84th minute before Manchester scored and the 84th to 89th minute before Fulham scored.

This is a possession breakdown of the 4 “games”. The team that won the possession battle during 3 of these 4 periods ended up scoring the eventual goal.

While not conclusive, it’s very clear that individual goals (not necessarily game results) are connected with possession statistics in some way. A simple 90 minute possession statistic of 57% to 43% clearly doesn’t tell the whole story of this 2-2 draw.

Click Image to Enlarge.

This is a time-series that shows the rolling average of possession percentage over the course of the game. This shows the ebbs and flows of the game with considerably more granularity than grouping by the 4 goal times. Understandably (and expectantly), this shows that goals seem to cause dramatic inflection points.

Also, the Manchester surge somewhere between the 30th minute and Fulham’s 54th minute goal helps explain why the 3rd column in the previous graph isn’t so heavily skewed in Fulham’s favor.

Now, the real question is how some of these trends fare on the larger season-wide scale.

I wrote a few scripts that calculate possession percentages for each previously defined sub “game” over the course of the season.

For all goals that resulted in a team gaining a lead (172 of them), the distribution of possession percentages of the scoring team is as follows:

While the 45% to 50% possession is the largest bucket, this distribution of goals is very clearly skewed to the right, suggesting that possession does indeed correlate positively to scoring lead-gaining goals. Also, 55.8% (96/172) of go-ahead goals were scored by teams that held over 50% possession in the time leading up to the goal. I think this is pretty significant.

By looking at goal distributions filtered by particular game states, we can begin to get a clearer look at possession statistics.

However, I am still very cautious of some of these findings. I believe that there remains plenty to be said about teams that employ approaches that are “more Stoke than Samba”.

In order to score, a team must significantly risk losing possession of the ball. In Barcelona’s example, when they are a playing against a much weaker opponent, worthy risks come up more often – therefore they are more likely to exchange possession for a scoring opportunity. In games that there aren’t as many opportunities, they retain the ball for longer periods of time.

In reality, teams do not to trade goal scoring opportunities for a larger share of possession.

Understanding Asymmetry – Fulham 2012


Not all 4-4-2′s are the same. Some have flat midfields and some have diamond midfields. Some have a pair of strikers and some have strikers deployed one on top of the other. Some have central midfielders who drop back into the defensive line, and some have central wingers. It should come as no surprise that some formations are also asymmetrical.

The modern game has evolved, yet our naming convention remains heavily rooted in the dark ages. The rise of the 4-2-3-1 is helpful because it recognizes that players are beginning to play “between the lines”, but there is still significant room for growth.

As a youth coach I have noticed something about the general manner in which we train our youth players. The first 14-15 years of their lives, we are training them how to “play their position”. Afterwards, we are burdened with trying to get them to play dynamically. It is easy to play against a “cookie-cutter” 4-4-2. It is much tougher to play against a 4-4-2 that is so tailored to a particular team’s strengths that it is hard to actually classify them as a 4-4-2. In this post, we are going to look at Fulham’s current 2011-2012 campaign; especially the interesting relationship between Clint Dempsey (#23) and John Arne Riise (#3) and the team’s general asymmetry.

This is my first post that introduces passing network graphs. By placing each player into their average position on the field, and drawing a line between each player that’s thickness is equal to the number of passes exchanged between the players, we get a very interesting look at how a team preformed during a particular game. In order to reduce the amount of noise in the visualization, there is a threshold of 4 passes for a line to be drawn. Also, the size of each player’s circle (node) is equal to the positional deviation of that player over the course of the game.

Case 1: Fulham vs. Everton (1-3) | Gameweek 9

For Everton’s graph for the same game, click here

The main thing that I want to draw attention to is John Arne Riise (#3) as the outside fullback and how much further up the field he plays than his right-back counterpart. Also, Riise and Clint Dempsey’s (#23) circles are larger than any other player – meaning that they tended to patrol the largest amount of area.

After looking at a lot of these graphs, it would seem to be that Fulham is playing a variation of a 4-3-3 in this match, with some very interesting quirks. Up the left flank, Riise is playing higher (or at least as high) as the three central midfielders. This allows for Dempsey to pinch inwards, acting as an inverted winger.

Since the two more traditional strikers, Zamora and Johnson are deployed more centrally and Riise and Dempsey harassing the left wing, it seems that Fulham is playing completely without a right winger – and it looks very intentional.

Also, it seems that Fulham tries to compensate for Riise’s forward deployment by playing Steve Sidwell (#4) as a holding midfielder.

Case 2: Fulham vs. Chelsea (1-1) | Gameweek 18

For Chelsea’s graph for the same game, click here

This is a very interesting side that Fulham fielded against a high-pressure Chelsea side. Notice Riise’s much more subdued deployment and the lack of a clear holding midfielder (Dembele, Murphy and Dempsey are certainly not holding midfielders).

Yet, it is still very clear that Fulham aims to attack up the left flank with the rare deployment of a classical left-winger in Kerim Frei (#21) instead of the hybrid system along the left that Dempsey usually shares with Riise. Clint finds himself still on his natural left side but in a considerably different role.

Case 3: Fulham vs. Bolton (2-0) | Gameweek 16

For Bolton’s graph for the same game, click here

I included this side because I felt that it was one of the more aesthetically pleasing graphs. It is perhaps not a coincidence that I feel similarly of how Fulham played on this particular outing.