.@TiagoEstv uses Opta data in this article for @StatsBomb, in which he assesses upcoming prospects in @ligaportugaltwitter.com/i/web/status/9… 11 Dec

OptaPro's data scientist @Worville will be speaking at the 'Getting your Data skills noticed - Data Science and Vis… twitter.com/i/web/status/9… 6 Dec

.@EuanDewar uses Opta data to dissect and discuss Atlético Madrid’s attack this season. bit.ly/1Hnj29V. Ailing. 4 Dec

.@jair1970 writes a 12-game Premier League shot analysis, focusing on teams’ performances in relation to previous s… twitter.com/i/web/status/9… 27 Nov

.@OptaPro will host a panel tomorrow at #SAC17 with @soccerquant, Ben Stevens (@CPFC head of performance analysis)… twitter.com/i/web/status/9… 23 Nov

.@EuanDewar and @EveryTeam_Mark co-author this data-driven article that explores Manchester City's impressive start… twitter.com/i/web/status/9… 23 Nov

The latest #ThreeAtTheBack episode outlines further information on @scout7football becoming part of OptaPro.… twitter.com/i/web/status/9… 17 Nov

The OptaPro Blog

RSS Feed

Welcome to the OptaPro blog, featuring news and analysis from OptaPro's cutting-edge research team.

COLUMBIA BLOG: Scoring Possessions

Since early 2011 student researchers from Columbia University in New York, led by Professor Casey Ichniowski, have been carrying out qualitative analysis of numerous leagues and seasons of full Opta data in an attempt to further contextualise events that occur within a football match. Casey and his team have used a range of statistical methods to analyse the data. The result has been a number of short-form analytical blogs, covering many different aspects of the game including 'Team DNA', scoring possessions and tempo. We invite comment and debate on each.

The first blog for OptaPro from Professor Ichinowski's team is another perspective on possession:

The number of possessions teams have in a game can differ by one at most. What a team does with its possessions ultimately determines who scores and wins. Clearly, possessions that move closer to the goal are more likely to score. With this simple logic, we develop a method to quantify the quality of possession.

Using data from 2005-2012 from all EU5 premier clubs, we plot all 19,018 regular play goals on a soccer field. We then scale the values at each x-y coordinate such that 100 represents the location where the most goal shots are taken and 0 represents no goal shots. The result is the following heat map.

Image1

Although an event directly on the goal line should probably be awarded many points, there are very few goals here simply because of the difficulty of reaching such locations. An award system purely based on goal scoring location may not be ideal as it also does not award places that are one pass or chip away from a goal-scoring opportunity, such as near the corners. To account for this, we look at the inverse frequency of all passes (and set all values in own half to 0). This essentially rewards teams for being in heavily defended areas that are difficult to be in and are in range of a goal or a pass leading to a goal.

Image2

We then combine the goal map and the inverse passing to to develop a hybrid scoring system for rating events. The resulting points map (rescaled to a 0-100) is as follows:

Image3

We now define Possession Score as the maximum point value of a pass or receive event in a possession. Thus, the quality of a possession is characterized by the most dangerous area it entered during the possession. (Alternatively, one can do maximum point minus points at starting location).

Looking at possessions with at least 4 passes, we calculate the mean Possession Scores for all club teams in 2011-2012. Despite their tiki-taka style, frequency passing and time spent midfield, Barcelona comes out on top as most of the possessions eventually lead to dangerous areas.

Image4

Now let us know your thoughts. Comment below:


Posted by Columbia University at 17:41

0 Comments

Post a comment