In recent years, we saw pass completion rates held up as an indicator of performance on the football field. A player with a high pass completion percentage was held up as an example of a good passer of the ball. While being able to complete a pass is, of course, an important trait for a player, it does not take into account the quality or importance of that pass. In an isolated game, for example, we could have two central midfielders playing in similar roles as a part of the double pivot in a 4-2-3-1 structure. Player A could end the game with a +90% pass completion ratio while Player B had a pass completion in the low 80% range. Basing our judgement purely on this statistic, we can be sure then that Player A is the better passer of the ball, right? No, Player A spent the match receiving the ball from the central defenders and then playing either backwards to the defenders or goalkeeper or sideways to the second pivot or to the near side fullback. Player B, on the other hand, was more progressive with his passing. He received the ball and displayed a tendency to look forward for opportunities to progress the ball through the thirds. His passes travelled into the final third and broke the opposition defensive structure.
So, which of these players added more value to their team? Player A kept possession but did nothing with it, while Player B gave the ball away more often but created far more opportunities for his team to score by advancing the ball towards the opposition’s goal. Since we know that the end goal in a football match is to win by scoring more goals than your opponent, we can, therefore, be confident in stating that Player B added more value to his team than Player A, despite a lower pass completion percentage.
This entire thought process led to the creation of a German company called Impect. This company was created on the premise that the data used to assess and evaluate football players was geared unfairly towards attacking players who scored or assisted, or midfield players who played safely rather than progressively. They therefore created a new metric called packing. The premise behind packing is simple but incredibly effective. A player receives a point for every opposition player that their pass or dribble bypasses.
Take the example above. If the player in possession of the ball plays the ball along the patch marked as 1, then they receive no packing points because no opposition players were bypassed. If they take the path marked 2, however, they gain three packing points because the pass bypasses three opposition players and breaks the opponent’s line.
The beauty of packing, however, is that it does not purely take into account the ability of the player passing the ball. As pass, of course, needs two players to be completed. The player passing the ball and the player receiving the ball. A separate score within the packing data then gives a point to the receiving player for each player that the pass bypasses. So, back to our example above both the passer and the receiver are given a score of 3 for the action shown. The passer is given +3 for opposition bypassed and the receiving player receives +3 for the opposition bypassed received.
This takes into account the importance of players in advanced positions being able to identify and occupy spaces between the lines of the opposition. A progressive pass that gains a high packing score is less likely to be effective if the ball is played into a contested area and it not completed.
One of the most prominent examples of this in football today is that of Roberto Firmino at Liverpool. It is widely known that Firmino is a player who tends to drop off the front line to occupy the ‘10’ space. This, of course, allows the likes of Sadio Mane and Mohamed Salah to move inside to the central space that has been vacated by Firmino dropping deep. There is, however, a separate function for this movement by the Brazilian international. This movement allows Liverpool to occupy the key space between the defensive and midfield lines of the opposition and the ball can, therefore, be played into this space and from here Liverpool have an advanced platform from which they can attack.
We see an example of this here as Liverpool built up through the thirds of the field. The first pass receives a packing score of 0 because no opposition players are bypassed. The second pass in the chain is assigned a score of 1, and we now see the importance of movement in advanced areas. We see that Roberto Firmino has moved back towards the ball and is occupying space. The player now in possession, Trent Alexander-Arnold, then finds a passing lane to the feet of the Brazilian. That pass carries a score of 5 for both the passer and the receiver.
Immediately we see the correlation between the packing score and the value of a pass. Each pass in that chain had intent and added value to an extent, but the key pass was the one that advanced the ball into the final third and into a central position.
How, though, does this translate into data at the professional level? Thanks to the kindness of Impect, we have been given access to the packing data from the German Bundesliga so far this season. We have split this into position groups so that the data can be presented and analysed in a more logical way. This data analysis will break this concept down for you.
Central Defenders
This chart is very simple: it shows the volume of passes each player plays that bypass an opposition player, and compares this to the number of passes that bypass five players closest to the opposition goal.
The chart immediately passes the eye test as we would expect to see David Alaba and Mats Hummels featuring prominently, given that they are widely regarded as two of the most progressive passers in football. David Alba averages 79 bypassed opponents per game and 5.3 bypassed defenders per 90. Mats Hummels, on the other hand, averages 71 bypassed opponents per game and 5.3 bypassed defenders per 90.
This data, of course, is also affected by each team’s style of play. It is normal for the likes of Bayern and Dortmund to have a lot of possession of the ball, and therefore, we would expect them to have players with strong passing numbers. It can be interesting to consider how these team styles vary across the Bundesliga.
Here, we see each player in the above chart grouped by their team. This gives us an insight into which central defenders are the most active in progressing the ball for their sides. We, of course, see Bayern and Dortmund as prevalent, but there are also interesting differences. Take Leverkusen, for example, where Jonathan Tah progresses the ball well, but Bender is less progressive. Union Berlin are on the opposite end of the scale to Bayern and it is clear that they do not progress through their central defenders at all.
This kind of information can be extremely interesting in terms of opposition analysis and planning for the next match. We very quickly see which players to target when in possession for each side.
Central Midfielders
Now let’s look at central midfielders. This chart looks at the number of players bypassed by each pass, but this time compares it to each player and their received passes in terms of how many passes those bypassed. Again, it passes the eye test very quickly with Thiago and Joshua Kimmich of Bayern Munich featuring prominently. Interestingly, Charles Aranguiz of Bayer Leverkusen is also featured, and a matter of weeks ago, it looked as though Bayern were close to finalising a deal to sign the Chilean international.
Again, we can use this data to identify team style as the ball passes through the midfield. Once again, the key outliers are Bayern Munich, with Thiago and Kimmich both excelling in terms of the number of players bypassed per 90. Interestingly, while Dortmund progressed actively through their central defenders, they are less active in the midfield band, with Axel Witsel showing only average numbers.
Once again, we can see that this is an important tool for gauging the danger of the opposition. Being able to identify which player is likely to progress the ball efficiently will tell you who to close down aggressively, while other players may be allowed space on the ball as they are not progressive passers.
Attacking Midfielders
Finally, we will assess the data on attacking midfield players, and again, we will start by looking at the number of players bypassed per 90 and the number of players in the defensive band bypassed per 90. Thomas Müller, Bayern again! He stands out with 13.9 defenders bypassed per 90. On the other end of the scale, both Jadon Sancho and Marcel Sabitzer are clearly performing well in terms of their ball progression metrics.
Something a little different this time, as we shift the focus on team style. This time we are looking at which attacking midfielder receive the ball from progressive passes that break the lines of the opposition. Again, Bayern Munich clearly stands out with five players that are above average in this metric, and Thomas Müller and Kingsley Coman are at the top of the chart. They are, however, joined by two young players who are highly regarded, Julian Brandt and Kai Havertz. Both profile differently in terms of their movement patterns and the positions on the pitch they occupy, but both also have an innate ability to drift into pockets of space in dangerous areas before receiving the ball and threatening the opposition’s penalty area.
This tool is again useful for player and opposition analysis. Still, it can also provide significant value in recruitment, identifying undervalued players who help their team progress the ball.
Conclusion
The concept behind packing is a relatively simple one but it is extremely effective. As a tool, it can inform player development as we teach young players the importance of passing in a progressive manner to add value to our team. It can help our analysis of opposition and scouting reports of individual players, as we identify which players pass and receive progressively, which can either hurt us or add value as a recruitment target.
