Identifying Attacking Types with Data: The Key to Optimize Offenses & Defenses

Picture from Murals your Way

Any type of analysis consists in first identifying general patterns that lead us to the more important aspects, and then take a deeper dive and find an improvement opportunity.

For example, to analyze a team’s strengths and weaknesses, the first thing we do is see how well the team is attacking and defending in different situations: set pieces, counter attacks, possession play, etc.

For a video analyst it’s relatively easy to identify game situations. Even if there are no manual definitions, we can recognize a possession pattern just by looking at it.

This looks really simple. But the problem is when this manual task is used to analyze many teams’ full seasons or watch all games from players to be scouted.

This is when identifying game situation with data becomes really helpful. By doing this, we are able to reduce a lot coaches’ time by finding general patterns and filtering situations for them to get the most important conclusions with video tools.

Let’s first look at the different types of attack and then see some examples on how we could use it to make teams’ diagnostics.


1. Set Pieces

2. Counter Attack

3. Fast attack

4. Direct Attack

5. Possession attack

From data to reality

In these diagnostics we will use the following metrics:

  • Expected Goals (xG) percentage from each type of attack to analyze teams’ offensive and defensive trends.
  • Expected Goals (xG) for every 100 possessions that end up in a shot to measure the danger created from each type of attack.

The following analysis correspond to the first 8 weeks from Argentinean Superliga 2019–20.

1. San Lorenzo’s Possession Game

2. Fast Attack: Velez’s Lethal Weapon

3. Racing’s Dependance on Set Pieces

4. Estudiantes’ Counter Attack Problem

After this type of diagnostics, we can guide our analysis towards something deeper, depending of what we are trying to solve or optimize. This following analysis could include:

  1. Starting possession zones.
  2. Shots & assists zones.
  3. Players involved in each kind of actions.

Once we are done with this exhaustive data analysis, now the video analyst can focus only in the improvement opportunity without watching and analyzing manually all chances.

On the other hand, this kind of information can be really helpful in scouting. For example, when we want to look for an offensive player, we can filter not only by their xG, but now we can search by their values in playing styles that are similars what we want in our team.

Data is changing football paradigms and the best teams in the world are already using it to make smart decisions.

If you’re interested in incorporating data analysis in your team, please visit our website and write us to or via Twitter.

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