I have spent the last few weeks chasing what I thought was a very elegant understanding between the number of possessions in a game and it’s result. I’m hesitant to call it a failure, but I have certainly left this exercise with many more questions than answers.

Essentially, my argument was that as the number of possessions in a game decreases, the liklihood of an upset increases. This is simply an extension of the fact that a weaker team is more likely to win against a stronger team in a single-game series than in a multiple-game series.

Despite finding that possessions per game seems to be normally distributed:

My first hint that I was wrong came in the fact that goals per game was actually negatively corrolated to possessions per game (albeit weakly). I expected at least a positive corrolation – perhaps even a strong one.

Then, I ranked each EPL team on the number of possessions per goal and also looked at the standard deviation of game-over-game possession efficiencies.

While possession efficiency expectantly corrolates strongly to league position, I was concerned with the large standard deviations. Increasing a seasonal scoring efficiency will obviously help a team in the long run, but seeing this play out on a game-by-game basis seems unlikely.

Here is a list of teams sorted by their average number of possessions per game and the associated z-score. The results here are interesting.

The league-wide average number of possessions per game was: 213.4105 and the standard deviation was 22.1311

The outlier in Bolton Wanders with signifigantly the largest z-score is pretty interesting. If I saw a prototypical team such as Manchester United or Stoke in this situation, I wouldn’t be surprised. What’s up with Bolton?

Also, given the original premise that weaker teams want to decrease the rate of possessions (and conversely for strong teams), why do we see Manchester United, Manchester City and Chelsea occupying 3 of the lowest 4 positions in average possessions per game? Shouldn’t they be the teams that would benefit the most from increasing the rate of play?

The first thought that comes to mind is that stronger teams find themselves in winning situations more often, and therefore actually can benefit more from slowing the game down.

By looking at the total number of possessions that a team had in losing situations, divided by the number of goals scored from those losing situations, we can get a picture of scoring efficiency when we expect it is in the best interest of the team to push the rate of play. More importantly: how each team’s efficiency changes based on the game state.

While I was not surprised to see Manchester United’s efficiency improve from one goal every 98 possessions to a staggering one goal every 73 possessions when losing(a 34% change), I was incredibly surprised to find Chelsea and Liverpool bottom-dwelling. What would cause such an incredible disparity between top clubs?

This metric is a decent measurement of a team’s ability to chase a game. Apparently, this quality isn’t necesssarily required to finish in top league positions.

I wonder if I could find a corrolation between the number of fans leaving early and this derived metric!

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.