Centre forwards are amongst the most difficult positions to recruit in football. Ask any coach of any club and they will tell you that they are looking for a striker that will bring them 15 to 20 goals in the upcoming season. Simple, right?
The issue is that there is more to a forward’s game than just ‘goals’, and in order to score goals, a striker will typically have to play in a team that suits their strengths and style of play. There are exceptions to the rule, of course, with the likes of Robert Lewandowski or even Dušan Vlahović being likely to score goals in a variety of systems. For the most part, though, a large part of the recruitment process for centre forwards has to revolve around the way that you want to play — or perhaps, more importantly, it has to centre around the way that you are actually playing, as there is often a disconnect between the perceived game model and what is actually happening on the pitch.
I still remember working for a club a few years ago when a new manager took charge. His brief, as is so often the case, was that he wanted a “tall striker”. That was it. That was the brief. No other information was forthcoming. I produced a list of eight strikers — all of whom were over 6’2” — and was thanked for the work that I had put in. A striker was then signed who was 5’8”…
This was a while back before data analysis was as widely used as it is now but it serves to illustrate the importance of information sharing as part of the recruitment process. The more information and context that the coaching staff can give you, the better, in terms of identifying the best metrics that can be used for identifying strikers.
In this article, we will run through a couple of case studies using teams within the English Premier League in order to show the different ways that you can interpret team style as part of the player recruitment process.
First up: Brighton and Hove Albion
So, let’s start with Brighton. The radar above shows their performance so far this season when compared to the league average. The raw data has been run through a code to produce percentile rankings to more accurately show how the team in question has performed.
As you can see from the radar above, Brighton are performing well in the passing metrics with outputs for possession, passes to the final third per 90 and progressive passes per 90. In the attacking section of our radar, however, the picture is less positive for the team from the South Coast. While they are around the league average for xG per 90 and shots per 90, they fall short when it comes to goals per 90 and the % of shots taken that are on target.
From the information in the paragraph above alone, we can then start to build a picture of the types of striker that Brighton should be looking to sign at this point. They do not need a striker who will necessarily participate in the build-up phase, rather they’re looking for a striker who is effective in front of goal. The next step, then, is to look at our data set. For the purposes of this article, we will consider all forwards in the top-five European leagues who have played at least 750 minutes so far this season.
So, let’s start by assessing the % involvement that the players in our data set have had in their team’s goals. Using % rankings such as these allow us to account for overall team performance. If we are only using raw data, for example, then it would make sense for a player who plays for a top-6 side to have scored more goals than a striker in a bottom-6 side (this does not always hold true but generally does). To allow us to compensate for this, we assess each player in the context of their own team by getting the % of their involvement in any of our key metrics.
As you can see, however, assessing data in this manner does not necessarily mean that the top players will not be at the top of our rankings and Kylian Mbappe of PSG is still top of our list followed by the exciting attacking midfielder Christopher Nkunku of RB Leipzig and then Dušan Vlahović — although the data is muddied somewhat here by the fact that he spent the first half of the season with Fiorentina.
The first player that I think is a match for Brighton in terms of what they are looking for and their likely budget is the French striker Amine Gouiri of Nice with 25.38% contribution (scoring and assisting) for his team.
Next, we will use a similar method to calculate how involved the players in our data set are in their team’s expected goals. Once again the player that we have identified following the first stage of data analysis, Amine Gouiri of Nice, features prominently in this list and again he is fourth in the ranking as he is involved (xG and xA) in 22.64% of Nice’s expected goals so far this season.
The above would be enough for me to feed this information back and to produce a data profile for the forward in question.
As you can see from the data profile above, Gouri has been performing exceptionally well for his side so far this season across the attacking and ball progression sides of the game. This is the type of profile of striker that we believe Brighton should be looking at going forward in order to help take them to the next level.
Now, let’s go through the same process for Burnley.
As you can see, when we look at Burnley through the prism of data they are in a notably different position to Brighton. They are below league average in the build-up phase in the likes of passes per 90 and possession. They are also well below league average across all of the attacking metrics on our radar.
While a goalscorer should, of course, be of interest to Burnley at this point, they need to start from a more basic position than Brighton in looking for a striker who can generate shots at goals and who will offer them a focal point in the attack.
So, this time we will start to look for strikers within our data set who are responsible for a large proportion of their team’s shots per 90. Burnley need to find forwards who get into areas that generate goalscoring opportunities.
This time the player on our ranking that stands out is the 22-year-old Senegalese forward Ibrahima Niane of Metz. Niane is actually a player who I have covered for this magazine in the past given his performances when fit for a Metz side who have struggled in recent seasons. Niane, unfortunately, suffered a significant injury, and that is probably the only reason why he is still with Metz. He is a powerful and quick forward player.
This time we are looking at the % of contribution that the players in our data set have had in terms of their team’s touches in the opposition penalty area. Here we are looking to show which players will provide a focal point for the attack.
Once again, Ibrahima Niane shows up well in this model and he is actually top of our rankings as he is responsible for 33.2% of his side’s total touches in the opposition penalty area.
Ibrahima Niane has a less impressive data profile than we saw with Amine Gouri but this is likely a result of his team’s relatively poor performances so far this season as they sit 2nd from bottom in the French top-flight. This, however, represents an opportunity for teams in terms of recruitment to sign a player who has good underlying numbers.
There are no straight lines in recruitment. Context is important and understanding what you value in a specific position and role can allow you to find value in the market. Finding a striker is not a straightforward process but the more information that you can feed into the process the better your results will be.
