In the modern game, the ability of a player to pass and progress the ball is becoming more and more important as the seasons go on. Players today are far more defensively aware than they once were and with the increase in physical capacity across the board at the top level, we now see players in the possession phase coming under more and more pressure. Even central defenders and fullbacks have less time on the ball now than they used to, even a few years ago. This means that players in all positions have to be comfortable receiving and playing the ball either around or through the defensive block of the opposition.
This means that players who have the ability to play passes that break the line of the opposition and progress the ball up the field are becoming inherently more valuable as the years go on. These passes can be line breaking, in that they play through into the middle third of the pitch, or diagonal/direct, in that they switch the play and move either closer or to the final third of the pitch.
Using statistics to identify progressive passers is relatively simple, right? Download the data from your data provider of choice, connect that data to your code or to a platform like Tableau and you can start to build visualisations that show the most progressive passers in your data set or those that play the most passes into the final third if you prefer. The issue that can arise here is that the data in your dataset will be skewed towards the teams who are most successful in possession dominance.
Lets take that point though and break it down a little. If player A plays for a possession-dominant team who are challenging for Europe, and player B is playing for a more defensive side who are fighting against relegation then it is likely (not certain, but likely) that player A will attempt more passes per 90 than player B. Lets say player A attempts 60 passes per 90 while player B attempts 28 passes per 90. It is clear that player A will have far more opportunties to play that progressive pass that we like.
In order to get around the performance bias that is in our data set we can use a relatively simple trick that allows you to gauge the potential effectiveness of players at teams who finished towards the bottom of the table or were even in teams who were relegated. I like to use a model that shows me which percentage of a teams total progressive passes over the course of the season each player is responsible for. So, with Player A, who averages a high volume of passes, we can realistically assume that his team as a whole averaged a higher volume of progressive passes than the team that player B plays for. This changed the picture within the dataset and starts to bring players to the fore that we can assume would benefit from a move to a team who perhaps better fit their skill set in their position.
In this data analysis, we will use this technique to show three players, in three different positions, that we believe are undervalued and that are good ball progressors. In this analysis, the data set that we are using has players from the top-5 European leagues. We have filtered this to show fullbacks, central defenders and central midfielders and we are looking for players who are 23 years old or under and that played at least 1200 minutes over the course of last season. Our data is all from Wyscout and the visualisations are created using Tableau and Python.
We will identify one fullback, one central defender and one central midfielder.
The Fullback
First things first, we will be discounting the presence of Liverpool fullback Trent Alexander-Arnold from our analysis because, as impressive as his performances were last season, he is anything but undervalued. Now, we are left with a group of very interesting young fullbacks.
I have previously written on this side about Fabiano Parisi of Empoli, Issa Kabore of Troyes, Maxime Busi or Reims and Melvin Bard of Nice but this time it is a relative newcomer who has caught the eye. Well, I say caught the eye but it is difficult to ignore the player at the top of the whole ranking, above even Alexander-Arnold, in Quentin Merlin of Nantes in Ligue 1.
Lets pa
