Developing New Metrics: Passes Received in Zone 14

In this new series, Josh Hobbs explores some of the novel metrics we have been able to develop using the TransferLab algorithm.

One of the perennial problems using data to analyse football is the discrepancy that exists between those with the ability to manipulate data and those who understand what is taking place on a football pitch. Where data scientists will be able to build models or scrutinise the numbers, they may not know how to structure their models or implement their findings on the training ground. Equally, where football coaches will be able to break down the game into phases of play or recognise rehearsed patterns, they may not have the ability to make the data coherent in a way that helps them in their jobs.

At Analytics FC, we have attempted to overcome this problem by building a model which begins to interpret the raw data. Using our unique TransferLab algorithm—a Markov Chain model—we are able to take events that happen on the football pitch and ascribe them a numerical value which indicates how much an action improved that possession’s probability of ending in a goal (and just as importantly, how much it reduced the other team’s chance of scoring on the next possession).

For example, a player receives the ball in central midfield. At this point, the team might have a 1.5% chance of scoring at this point in the possession and also a 1% chance of conceding on the next possession. That situation isn’t very valuable. But if the player executes a dangerous through-ball into the final third, the team is now in a much better position and might have a 6% chance of scoring and only a 0.5% chance of conceding. The pass would be worth the difference in their team’s situation before and after it. 

The algorithm calculates every action and can therefore determine the overall impact a player has through all their actions and the impact is presented to clubs in terms of “Goal Difference Added” per 90.

The beauty of this approach is that it allows you to farm the data to find the sorts of actions that are creating value for teams and model your game style to reflect that. If a certain pass into the box is proving a good source of value for a team, then the coach might want to think about ways to encourage those sorts of actions within the game. Suddenly, data and coaching are much more closely aligned.

In this series, we are going to explore some of these actions that add value to a team’s game.

Receiving in Zone 14

When looking to create goal-scoring opportunities, perhaps the most important place on the pitch to focus on is ‘Zone 14’, the central area just outside the penalty box. 

From this zone, passes into the box are more likely to end up as a goal. It’s also where the pass that leads to an assist comes from most often. 

It’s because of this that players who operate in this area of the pitch are so valuable. If you have a regular creator from this position, your team has a far greater chance of scoring more goals.

Who could be better to illustrate this than Leo Messi? 

When Pep Guardiola first decided to utilise Messi as a False 9, getting him on the ball in Zone 14 with wide forwards making diagonal runs inside to receive his passes and score was central to his plans for success. 

It’s safe to say that it worked. Messi blossomed into arguably the greatest player of all time and Barcelona went on to enjoy an era of dominance. All these years later and this is still the area in which Messi does his best work. 

Finding the Top Performers in Zone 14 in TransferLab

Using the TransferLab algorithm, it is now possible to find players who add the most value to their team’s goal difference by receiving the ball in this area.

In TransferLab, we now have access to a new metric: ‘Zone 14 passes received (Quality)’. Sorting our database by this metric and filtering for players who have a minimum of 720 minutes in TransferLab’s top three tiers of competition, these are the top performers for season 20/21: 

It’s no surprise to see elite creators like Messi, Neymar, Angel Di Maria and Bruno Fernandes make the grade. However, the rest of the list is made up of target man-type centre forwards with Wycombe Wanderers’ hero Adebayo Akinfenwa actually showing up as the top performer.

Initially, this might seem surprising. But of course, the idea for a target man is to get the ball to them in Zone 14. From there, they will be competing for high balls which they will attempt to flick-on for teammates to run on to, or alternatively, to receive the ball with their back to goal and look to link with a strike partner or runner from midfield.

This assist from Artem Dzyuba for Sardar Azmoun is the perfect example of the latter: 

We can see, then, that there are two different player types who thrive in this area: target men and playmakers.

Strikers

Here’s how the list looks with only players with their primary position listed as striker included:

Lionel Messi is certainly the outlier in terms of player-type here. He is the only playmaker on this list. 

Of course, many users of TransferLab will be looking for younger players, so here are the results when adding in a filter for players 23 and under: 

Here we see a slightly different spread of player types. Kylian Mbappe, Jonathan David and Lautaro Martinez are certainly not target men and would be much more naturally associated with being a creative forward. 

A lesser-known player from this group who also aligns more closely with the others as a creative forward is Young Boys’ Meschack Elia. He averages 0.25 expected assists per 90 with through balls being his primary source of chance creation. 

The example below isn’t quite like the Messi example but it differs from the Dzyuba assist in the way he was able to receive the ball.

Rather than back to goal having to hold off the physical attentions of a centre back, Elia is on the half-turn in a few yards of space. The clipped through ball wasn’t executed perfectly and was subsequently cleared but it gives an idea of what he wants to do in Zone 14.

Attacking Midfielders and Wingers

We’ve looked at a few strikers who add value to their teams by receiving the ball in Zone 14. However, most recruitment teams looking to add some creativity in that area of the field would probably be looking for somebody operating behind a striker or drifting in from the wings. 

This image below features the top performers for attacking midfielders and wingers to have played at least 720 minutes in TransferLab’s top three tiers of competitions in 20/21. 

As they appeared in the first list, Neymar, Angel Di Maria and Bruno Fernandes were obviously going to be the top performers. It’s no surprise to see Kevin De Bruyne here either, although much of his creativity happens in deeper and wider positions than the other three mentioned. 

An interesting player on this list is Parma’s Dennis Man. Below are his numbers, predominantly from his time at FCSB last season:

The 22-year-old has only been in a top five European League since January after moving from Romania. Ostensibly a right-sided winger, the heat map of his passes shows that he likes to come inside regularly and he is clearly one who adds a lot of value creatively in  Zone 14. He will be one to watch carefully. 

Here is an example of him playing the ball to a wide area of the box that almost leads to a goal: 

Whilst he doesn’t have any assists from this area for Parma at this stage, as we’ve already mentioned, Zone 14 is also the area of the field where the pass before an assist comes from most often. Given he regularly makes movements like this to receive the ball, Man will often be involved in possessions that lead to goals even if he doesn’t make the final pass.

Filtering for players under 23 again returns this list:

The top performer this time will be familiar to Everton fans. Nikola Vlasic of CSKA Moscow rates highly as an advanced playmaker when compared to all other forwards in Tier 2 Leagues (the Russian Premier League is a Tier 2 league).

Though he would likely be expensive, the Croatian looks to be one who is more than ready for a step into a top five European League with his expected goals and expected assist adding up to 0.35 per 90. He should particularly be of interest to a side looking to increase their output from Zone 14 as this is the area of the pitch that he most loves to operate in.

Developing New Metrics

In this series, we will be covering a number of novel metrics we have developed using TransferLab’s algorithm. However, we can create any number of new metrics to determine whether certain in-game actions accrue or reduce goal difference in games. If you have any ideas about potential metrics, do get in touch with us and we’ll see what we can do to implement them.

Analytics FC provides software and data services to entities within football looking to realise the gains possible from analytical thinking. We provide cutting-edge software solutions such as TransferLab, which helps improve and simplify recruitment decisions. To find out more about TransferLab and our other data services, or to find out more about us, visit our website.

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