Unexpected Findings of Possession Efficiency

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.


Poss. PerĀ Goal

Std. Dev.

Manchester United 98.5769 64.07804134
Chelsea 110.3333 65.59348760
Arsenal 111.9583 63.99903954
Manchester City 127.9000 70.44894066
West Bromwich Albion 134.6786 63.68292183
Newcastle United 139.0179 74.94802860
Liverpool 139.2373 67.37557058
Tottenham Hotspur 144.8545 59.69067099
Blackpool 147.8364 65.99140546
Everton 161.2353 56.45666225
Aston Villa 170.8333 61.44454270
Bolton Wanderers 172.5962 73.02691820
Fulham 173.7959 65.40885368
Stoke City 178.9783 66.84436451
Wolverhampton Wanderers 182.8478 66.98477993
Blackburn Rovers 183.4565 64.24138325
West Ham United 183.8837 61.57649119
Sunderland 189.3111 66.84921414
Wigan Athletic 200.7500 65.84863113
Birmingham City 217.8919 53.02540996

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


Possessions Per Game

Z Score

Bolton Wanderers 236.1842 1.02903654
Sunderland 224.1842 0.48681315
Fulham 224.1053 0.48324589
Blackburn Rovers 222.0789 0.39168624
Wolverhampton Wanderers 221.3421 0.35839182
Stoke City 216.6579 0.14673445
Everton 216.3947 0.13484358
Liverpool 216.1842 0.12533089
Aston Villa 215.7895 0.10749460
Blackpool 213.9737 0.02544764
Birmingham City 212.1579 -0.05659932
Arsenal 212.1316 -0.05778841
Wigan Athletic 211.3158 -0.09465009
Tottenham Hotspur 209.6579 -0.16956253
West Ham United 208.0789 -0.24090771
Newcastle United 204.8684 -0.38597625
Manchester United 202.3421 -0.50012854
Manchester City 201.9474 -0.51796484
Chelsea 200.3421 -0.59049910

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.





Tottenham Hotspur 86.47 144.85 58.38
Aston Villa 121.71 170.83 49.12
Manchester United 73.25 98.57 25.33
Wigan Athletic 149.33 200.75 51.42
Everton 121.89 161.23 39.35
Fulham 137.76 173.79 36.03
Newcastle United 111.47 139.01 27.54
Wolverhampton Wanderers 150.16 182.84 32.69
Bolton Wanderers 143.83 172.59 28.76
Blackpool 123.64 147.83 24.20
Sunderland 160.92 189.31 28.39
West Ham United 192.64 183.88 -8.76
Manchester City 136.13 127.90 -8.23
West Bromwich Albion 146.00 134.67 -11.32
Arsenal 123.18 111.95 -11.22
Birmingham City 241.90 217.89 -24.01
Blackburn Rovers 204.00 183.45 -20.54
Stoke City 218.08 178.97 -39.11
Liverpool 180.22 139.23 -40.98
Chelsea 165.90 110.33 -55.57

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!

8 thoughts on “Unexpected Findings of Possession Efficiency

  1. I think you missed what affects number of possessions. The number of possessions in a game should be roughly equal to the total amount of interceptions (including saves the goalies holds), successful tackles, passes out of play, shots off target, fouls by the attacking team, and goals, because each of these constitutes a change of possession (I probably left some out). The three most frequent will usually be interceptions, tackles, and passes out of play. Because of this, if a good team controls a game, with a high passing percentage, the number of possessions will be lower than an average game. And if a team plays a direct passing style, like Bolton do, the number of possessions will be higher. Hope this helps.

    • Certainly! I don’t think I articulated specifically what I thought effects fluctuations in possessions per game.

      Games with large possession counts have a higher “rate of play”, which is almost synonymous with playing a direct style of football. I did find it strange, however, that Fulham was further up than Stoke. This suggests that style of play is not the only factor in rate of possession.

      In other words, what I am looking at in this article is the efficiency of these different styles of play in different game situations.

  2. Hi Devin,

    Congratulations on an excellent piece of work. As you know, I’m looking into possession-based analysis at the moment too, so I’ve got some thoughts I thought worth sharing here.
    To go back to basics before trying to answer some of the questions you raised here, what’s the definition you used for possession here? In my work I tend to look at any string of match event containing at least a deliberate action with the ball, i.e. a completed pass, a shot, a set piece, etc. And further, does a possession spell start over in your definition when a team for instance wins a corner, or does that constitute a continuation of the same possession?

    As always in analyzing football, context is everything and not every possession is the same. You can see that very well in your ‘losing situations’ table, where teams really show different patterns of possession efficiency. The same holds true for level match situations and leads. The score in the match dictates much of what’s demanded from a certain string of possession.
    Finally, also the team’s pressure on the opponent will influence the amount of possessions, as it influences the amount of time the opposition is granted with the ball. So simply counting the amount of possessions will always mix offensive and defensive aspects of the game.
    Again, thanks for this interesting post!

    • Hey Sander,

      I’m happy you found your way over to my blog, I always enjoy reading yours.

      To answer the question regarding my definition of possession (which is a question I expected), I treat possession precisely as you do (one deliberate action) – and currently am including set pieces as a continuation of the same possession.

      Hopefully that can help you dig a little deeper into my questions.

  3. Bolton: presumably this is largely due to winning lots of headers and giving the ball away quickly once they get it, meaning a large amount of changes of possession.

    Manyoo, City and Chelsea: more passing, slower tempo, more emphasis on retaining possession, rather than getting the ball forward quickly. Fewer changes in possession.

    You can only get another possession if you give the ball away, so a team that gives the ball away a lot will have a lot of chances to win it back!

    I wonder if it would be possible for you to show not the average possessions per team per game, but the average total number of possessions for both teams in each club’s games. So you could see if, as I suspect, on average a Bolton game has far more possessions overall than a Manyoo game. It seems fairly obvious that it should, because (unless I’m missing something) a team has to have almost exactly 50% of the total number of possessions in any game, given than any change of possession necessarily gives the other team the ball.

  4. Would it not make more sense to map time in possession to goals. As has been stated, number of possessions can increase for many different reasons.
    Using time in possession while ball is in play may show a correlation you expected.

  5. Hey,

    Love your blog. Tremendous insights. How are you defining possessions? What data source are you deriving these #’s from? As you may have seen from my website, these are questions that I am struggling with right now and I am interested to hear another take. Your possession #’s are significantly higher than what I’ve found so far in MLS games. Perhaps the EPL is that much more direct?

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