Tag: analytics

Packing

Posted on by Markus Breiter in Football, Performance

Modern football analysis is all about data science. Therefore, different models have been developed to describe and analyse the game properly. Last time, you got a little introduction into the pitch control model  to understand how you can manage to get in possession of the ball. In order to achieve your target, scoring a goal, you have to make progress with your possessions. One metric to measure the efficiency of your ball possession is the packing.

Packing was invented by the former German football players Stefan Reinartz and Jens Hegeler[1], who developed the metric with their start up IMPECT. The statistics was first recognised by a bigger crowd, when different, mainly German, TV stations used it in their daily match analysis in the European Football Championship 2016. The idea to it had its origin in Germany’s 7-1 defeat versus Brazil in the 2014 World Cup semi final, when Brazil dominated the game due to the “classic” football statistics, such as ball possession, but did not stand a chance.

A short definition of the need of packing is brought by the co-founder Reinartz: “It’s ultimately about retaining possession whilst getting past the opponents. The opponent is our problem and to solve it, I want to get past the opponent with the ball.”[2]

Packing measures the number of opponents that are passed through with a specific action, either with a pass or a dribbling. This means that in packing, ball possession and success are not necessarily related. With one good action, be it a well-formed pass or a powerful dribbling, you can surpass many opponents. On the other side, you can play hundreds of horizontal passes or dribblings, which means a high ball possession without any outstanding success. On the side, you can see an example of successful packing in a specific situation. With one well-timed pass to his teammate Harry Kane, John Stones bypasses eight opponents, gaining a packing ratio of +8.

The 2018 World Cup is a good example for the power of packing, when eight of the nine most successful teams in packing during the group stage made the cut to the quarterfinals.

Some experts like Mehmet Scholl even considered packing to be the “holy grail” of tactics and the key to unlock a successful game plan[3]. Surely, this is too much eminence for this analysis tool, but it is another puzzle piece which can help making you successful. But no pass or dribbling in this world has any value, if there isn’t somebody at the end of the pass or the dribbling who is able to finish the job and score the goal. How efficient somebody is can for example be measured with the xG model, on which you also can find some explanations on our blog. Only if you know how to connect all the statistics in a senseful and proper manner, you can be successful. But this is a whole art and science on its own. And all this to bring the round one into the squared more often than your opponent…

[1] https://www.akademie-sport-gesundheit.de/lexikon/packing-fussball.html

[2] https://inews.co.uk/sport/football/world-cup/packing-football-statistic-world-cup-england-v-sweden-173408

[3] https://www.watson.de/sport/interview/458713925-gibt-es-eigentlich-packing-noch-8-bundesliga-vereine-schwoeren-drauf

Space Creation

Posted on by Markus Breiter in Football

Modern football analysis is all about data science. Therefore, different models have been developed to describe and analyse the game properly. Last time, you got a little introduction into the xG model to understand a bit about the quality and use of created chances. To create chances, you first need to be in possession of the ball. One model to explain why a player can how and when get the ball, is the pitch control model, which will be introduced slightly now.

“Pitch control (at a particular location) is the probability that a player could control the ball, assuming the latter were at that particular location.” This is the definition by Liverpool FC’s leading data scientist, William Spearman. So the model describes the situation on the pitch in a particular moment. To get data out of the information pieces, you have to combine the different momentum pictures. Today’s standard creates 25 of such pictures per second, which leads to a data amount of approximately 2.5 million pictures per game that have are brought in order to create a match film. Those pictures are mainly filmed with cameras and transformed with AI into the pitch control model, which allowed coaches not only to use it in pre- or post-game-analysis, but also to use it for tactical changes during half time. The picture below shows, how such a momentum picture could look like. In this picture, the red team has little advantages in pitch control, but they are not big enough that they could generate obvious quick use of it.

Source: https://youtu.be/X9PrwPyolyU

Spoken easy, the player that has the ability to get fastest to the ball from his location is the player that controls the ball. The ability is determined by the player’s position, direction of movement and maximum possible speed. The target for a team has to be the control of most of the pitch or at least the most important regions of the pitch under its control.

As mentioned, not all points on the pitch do have the same value for a team. If team A is in possession of the ball, their own box is less valuable than any given point in their opponent’s half, because having the ball in team A’s box makes it a long way to score a goal. Therefore the “scoring opportunity” of every point on the pitch weights the value of the pitch control at various points. Such a model could for example be the xG model (for details check out our former blog article).

Pitch control does not necessarily lead to success. It is often related to ball possession, but there are numerous examples that neither pitch control nor ball possession automatically mean success. If a team for example play a well-formed counter attack strategy, they do not need much possession to be successful. No single metric can bring your team success. But the clear understand and wise combination of different metrics can higher your chance of being successful.

Data-based analysis in football on the example of the xG-model

Posted on by Markus Breiter in Football

Football is all about goals. And luck.” This quote is often used when some fans or coaches try to explain, why their team is not so successful at the moment. And this assumption is not wrong at all! In 2018, researchers of the TU Munich figured out, that 47% of all goals scored in the season 2016/17 in both, German Bundesliga and English Premier League, that been affected by some sort of luck [1]. But in a business worth a few billion dollar (for this check our article “Data in Professional Football”), Good or bad luck is never a good advisor. To get an inside about a team’s or player’s performance without the factor, some different indicators were developed. These Key Performance Indicators (KPI) determine the quality of a team or a player due to the desired aspects. Different KPIs can for example rate the distance covered during a match or the pitch control (learn more in an upcoming article). Also stats like pass quality or the exploitation of opportunities.

The latter can be measured with the expected-goals-method, short xG. This statistical model predicts the probability for a goal to be scored based on historic data. The parameters used for this model are, among others, the distance of the shot, the player’s angle to the goal posts and the number of players standing in the ball’s flight curve. The German Bundesliga uses an algorithm built by Amazon AWS, which has analysed 40,000 shots on goal to train its AI[2]. The xG-model rates the quality of a goal shot. For example, if team A has had 15 goal shots with an xG of 0.1 (could be 10 shots from central 20m to the goal) and team B has had 2 goal shots with an xG of 0.75 (2 penalties), than team A will probably with 5:2, if they have a world-class shooter from outside the box. Or lose 0:2, if every shot was taken by a player whose shooting skills from far outside are humble or every shot had been blocked. Taking the xG model, the game would end with a draw, namely 1.5 (15×0.1):1.5(2×0.75), although Team A has the odds ratio in their pocket with 15:2. This shows, that the result of a football game is dependent on many parameters, like the players’ quality or even luck. An especially crazy situation can be found in the 2019/20 season of German 3. Liga, where the 1. FC Magdeburg leads the table after 28 games and are close to promotion, if you take the xG-model as basis for all results. In reality, they sit on P 15 and have to fight against relegation, whereas the SpVgg Unterhaching faces the complete opposite, being on P2 with the xG-model having them on P15[3].

[1] https://www.sueddeutsche.de/muenchen/fussball-tore-zufall-forschung-professor-fc-bayern-dusel-1.4008709

[2] https://www.bundesliga.com/de/bundesliga/news/neue-echtzeit-spielanalysen-der-dfl-und-amazon-web-services-11247

[3] https://blaugelbedatenwelt.com/2020/06/01/3-liga-wahre-tabelle-xpts-und-die-xg-xa-elf-des-tages-28-spieltag/ (graphic)