So far in this series of articles we have used data analysis techniques to identify players who we think are undervalued in the top flights of Switzerland and Serbia. For a little context, we have defined the term undervalued as players who are not playing for teams who are at the top end of their respective tables or who are performing extremely well for teams who are further up the table. This is a reaction to the tendency, when working with data and using standard techniques like scatter graphs, for players who are playing for teams who are being successful to feature more prominently in your data set. While this is generally fine in that as a recruitment department we have to be able to identify these players and understand that they are performing well in the context of their league we also have to be able to find ways to find players who are performing well for teams who are struggling or players who are performing well and generating a large % of output for teams who are towards the top of the table.
It is not all that long ago that the recruitment department at Liverpool were getting criticised for signing the likes of Giorginio Wijnaldum, from Newcastle United, and Andy Robertson, from Hull City, despite both players being involved with teams who ended that season being relegated. As we now know, thanks to the benefits of hindsight, these signings worked out extremely well for Liverpool.
So, the lesson is simple. Sometimes within recruitment, we have to be able to look past the context of team performance in order to assess a players suitability for our club. This may sound like something of a mixed message as I have previously written fairly extensively on the fact that context is crucial within football recruitment. What we have to do is be able to understand and find the balance between understanding how the team context can influence a players output (if a team does not play out from the back then we have to understand that when assessing a central defenders ability to play out from the back) whilst also understanding that players within a team system can be assessed outside of their overall team context.
In this article, we will turn our attention to the Romanian top-flight and our data set includes all players within that league who are aged 28-years-old or under. We use data from Wyscout that is then converted to percentile rankings and into pizza charts using Python while the raw data is used to convert % rankings and displayed via ranked bar charts in Tableau.
The Analysis
First up we will look at how involved players are in terms of scoring and assisting for their team. To do this we use fairly simple calculated fields within tableau to find the % of the total team goals that they have either scored or assisted. This is a technique that allows us to quickly find players who are performing well in the attacking phase of the game.
At the top of our ranking comes the Universitatea Craiova winger Andrei Ivan who has scored or assisted 24.5% of his teams goals in the league so far this season. Next is Florin Tanase of FCSB with 23.56% contribution to total goals and then Rares Illie, a 19-year-old, at Rapid Bucharest who has 23.31% involvement.
While in this
