Possession chains and passing sequences


Few days ago,  I tweeted that the ‘newer concept’ of ‘possession chains‘ proposed by Marek Kwiatkowski (@statlurker) in his latest blog*  was ‘very familiar to me’.  I also attached  text taken from my website (www.soccerlogic.com), where I write of ‘passing sequences’: a similar (same?) concept to Marek’s ‘possessions‘.  When @SportsDataChal asked me if I had published anything on the subject, I replied that I had only showed graphics  on my website, and promised that I would publish more on my blog.  Since I have no time (and inclination) to write anew on the subject, my intention was to fish out past notes on the subject and publish them without any editing.

Possession chains/passing sequences/event chains/link-plays/…

That is what I am doing below.  First an extract from Marek’s blog where he introduces his ‘possession chains’, then my three pieces on the subject.  The first is taken from an unedited note (rant?) on football analytics, the second from a marketing document aimed at football clubs. and the third from a document/proposal submitted (then) to the Capello index developers. A graphic representation of passing sequences copied from my website, is shown at the bottom (Euro 2004, Portugal).

From Marek’s blog*

Luckily, a newer concept is emerging into view and taking a central place: the possession chain (possession for short). A possession is a sequence of consecutive on-the-ball events when the ball is under the effective control of a single team. A football game can then be seen as an (ordered) collection of sequences. It is a very positive development since possessions make much more sense as the fundamental building blocks of the game than events. This is because they are inherently dynamic — they span time and space. I believe that they should be studied for their own sake, and if you only compute them to figure out who should get partial credit for the shot at the end of it, then in my opinion, you are doing analytics wrong – or at least not as well as you could be.”

1. SoccerLogic’s Event Chains/Passing sequences – (2004)

“One of the main reasons to use a football analysis tool is to identify event chains, that is to identify what events that led up to a specific situation. For example, if a team scores it is Interesting to see what events that happened just before the scoring. For instance, a goal could have come after five successive short passes in a row in the team. It could also have come after that a defensive player lost the ball to an attacking player who shot immediately. To know what events occurred just before one goal is not very important but if there is recurring patterns in what kind of events that have occurred just before a goal, it is very interesting information. If, for example, goals very often are made after a number of short successive passes within the attacking team, the coach can draw the conclusion that a way of scoring is to use short passes in the offensive play.

The software program should be able to aid the match analyst in the identification of recurring event chains. A requirement for this is that there is a database of event chains from previous games, as described in the previous section. Some kind of event chains could possibly be identified in just one game, but in most cases several games have to be analysed in order to identify recurring event chains. A way of identifying event chains is to compare the five events (passes, shots, dribbles etc) that happened just before every goal and then compare if there are similarities.”

2. From a SoccerLogic marketing document to football clubs – (2005)

“One of Soccerlogic many useful features is a very effective method for analysing event chains or passing sequences. The purpose of this analysis is to find recurring passing patterns.  These provide crucial information for understanding a team’s style of play: the tactical/strategic elements of its performance.

SoccerLogic can display event chains leading to (and following) any key event of a match, such as a goal, a foul, a shot on goal, a cross, etc. in rich graphic details. It can also create summary views (trellis) of chains leading to any particular event; these make it easy to compare passing movements and identify recurring patterns.  For greater accuracy, event chains of many matches can be analysed together.  Computer-based statistical analysis is then used to find among them trends and patterns correlating to good/poor performance.  This information provides a coach with an objective assessment of the effectiveness of his decisions, and helps him devise winning strategies for subsequent games.”

3. A data-based method for assessing Team performance – (2011)

“Football performance analysis normally focuses on players, not least because their stats are easier to collect and process.  Judging a team’s performance is not so simple. Team stats normally published in the media (corners, shots, possession, etc.)  tell only a small part of the story.   Players are also the focus of the Castrol and the recent (and controversial) Capello performance index.  There are no similar indexes for Teams, which are normally assessed solely on form – Wins/Losses and goals scored.   Since in football the final result often does not reflect performance on the pitch, this is not a satisfactory way to judge a team’s performance.

Given the amount of match data that is collected today, I am surprised that nobody has come up with a better method.  I guess it has much to do with the lack of skilled sports data analysts to fully exploit this data.  So, I developed my own solution.  I think it offers a very effective way to measure team’s performance, and can provide interesting stats to the media, as well be as valuable info to coaches.

My method is based on the analysis of the Possession of each team.  A Possession is defined as a sequence of events (ball touches) which starts when a team gets the ball and ends when the team loses it to the opposition.  The method views a football match as a series of alternate Possession.  This is not unique to football, but can be applied to any ball game (basketball, hockey, rugby, etc.).  I think basketball is the only ball game that appears to be  analysed this way.  But, compared with basketball, football is a very low scoring game, so the challenge is to find a useful Measure of Performance (MoP).

A Possession is the true expression of team performance because it describes how players work together to achieve a goal, and is specified by the following attributes:..
(details follow)




About soccerlogic

Data analyst/miner of 23 years experience. Pretty sure I was first (1998) to apply Statistical Analysis and Machine Learning to study performance in soccer. I probably invented Soccer Analytics or, as I called it then, Football Intelligence. Haven't stop learning since, and experimenting new analysis that can help teams improve performance.
This entry was posted in Passing sequences, Possession chain analysis, Soccer analytics, Soccer match analysis, Sports Analytics. Bookmark the permalink.

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