Who are the best young centre backs in Europe’s top five leagues?
Thought I’d share a bit of work I completed recently using some Statsbomb data to investigate the best centre backs who are U26 from the Premier League, La Liga, Bundesliga, Serie A and Ligue 1.
The main ideas around the methodology have come from my player ratings system. However, this time I am using Statbomb data and different metrics, rather than Wyscout data which is what my original system is based on.
Using Statsbomb player data across the top five European leagues from the 2021–22 seasons, I have developed a player ratings system to rank players.
Eight attributes have been defined and weighted to create the ratings. These attributes are minutes played, defending, aerial, front foot, ball carrying, ball playing, ball retention and attacking threat.
These attributes are derived from weighted metrics which I personally feel are important.
I use the function “z-score” to get all the scores for each player and each metric. “This is a mathematical distribution which describes the position of the raw score in terms of its distance from the mean, when measured in standard deviation units.” — As mentioned above, you can read more about the methodology in detail here:
Let’s look at those attributes in more detail and outline what metrics go into them. Just to reiterate, this is by no means an exhaustive list or something which is fool proof, it’s just personally what I deem appropriate.
After each of these attributes are created, they are then weighted themselves to highlight the importance. Here is where you can be creative and say actually, we value ball carrying far more than aerially ability. That’s when you can go deeper into these rating systems and set up different templates for various roles in that position (for example ball playing CB, ball carrying CB, aerially dominant CB, and so on).
Looking at the above you can see I have a fairly level weighting between defending, aerial, ball carrying and ball playing. All personal preference here, but as I am trying to find well rounded players, I’ve gone for more of an even split.
Here we have a look at the top 20 rated players, for those who might want to look for a specific player to see where they rank, here is a full list of the ratings.
We can go more granular into looking at specific players, and their profile. Here’s two examples with Nathan Ake and Adam Webster.
For those of you that have read my first piece on player ratings, or perhaps seen my visuals twitter, I’ve made some changes so the charts show the rating values.
Here’s a couple more examples of players, this time we have Nico Schlotterbeck and Niklas Sule, who have both moved to Dortmund this summer.
As with anything like this, there is no such thing as the perfect system. Firstly, you always need the eye test in football. Data is amazing, and you can do a fantastic job and building up a long / short list, or use it to have a deeper look at a player. However, it does miss things, you need to add the context and understand the limitations.
As with all rating systems, there are going to be limitations. I thought it best to acknowledge these and highlight them.
Restricted to positions — Football is a fluid game. We often see players in different roles throughout the game regardless of what their position was at the start of a game, for example a centre back’s numbers are very likely to be impacted if they play 1/3 of the season at fullback, or in midfield.
Team strength — These ratings don’t take into account team or league strength. As a result you are likely to see more dominant teams have players closer to the top of the ratings. Ideally we would adjust the ratings for team strength.
What is important — These attributes, metrics and weightings are what I feel is important. It is very much open to interpretation to the individual creating the metrics.
I’m very much open to feedback, so would love to hear your thoughts. This is never going to be a perfect system, but I think it is a good start when looking at a big data set of players.
If you do like this sort of content, I post a lot more data visuals over on my twitter, so head over there if you want to see more.
