We are creeping ever closer to the point in which people within football can approach recruitment questions in the 2021/22 season without worrying about sample size. My own personal preference for sample size when using data is for there to have been ten matches played already in any given competition. That does not mean, however, that we cannot use data in the recruitment process before that magical ten-match point. Instead, we have to apply our own lens to the data and appreciate that what we are seeing will have to be verified as the season progresses. Indeed, using data at this early point in the season to get an early look at which players are performing well can be an efficient way to inform the ongoing longlisting and shortlisting process as clubs start to plan for upcoming transfer windows.
When I am using data in this manner my process now remains relatively static. I use Wyscout as the data source, the best option from a cost/quality/depth perspective and Tableau as the tool for visualising and interpreting the data. Using these simple tools I am able to gain insights into the performances of players and indeed clubs from across the football world. I have my own combinations of metrics that are used to identify players that are well but these are also combined with metrics that meet the specific requirements and style that the client club prefers. Having assessed player performances I then move on to the longlisting process using custom-built dashboards within Tableau. These will be shown below and allow me to quickly assess player performance across a number of key metrics depending on the position of the player in question.
This will be the first in a series of league specific data analysis pieces that I will be writing for the site. In these articles, the idea is to choose specific leagues before assessing player performance within that league and picking out the players that have impressed at this early stage of the season. At this point, I should stress that I am not suggesting that I would instantly recommend any of these players for recruitment. Rather that I would enter them into the process that would see them watched on video or in-person and then assessed again through their data at a later point in the season. I am not in any way an advocate for the use of data as the sole driver in the recruitment process but I do fully appreciate that it has its uses. A smart club will combine data analysis processes with more traditional scouting and recruitment techniques.
The first league that we will be looking at is the second tier of Dutch football, the Eerste Divisie.
#1 Damon Mirani, 25-years-old, Central Defender, Volendam and the Netherlands
I should start at this point by saying that Volendam are one of my favourite teams to watch in Europe. Under the coaching of the former international midfielder Wim Jonk they play in a style that you would normally associate with Ajax or Barcelona with positional play at the heart of their methodology and game model. This summer saw the club have to sell their talented 20-year-old central defender Micky van de Ven when Marseille made a bid reported to be in the region of £3.15M for his services. Volendam planned for the sale well and the recruitment of Damon Mirani from Almere City appears to have filled the hole extremely well.
Mirani is a product of the Volendam academy but he left the club as a teenager having been recruited heavily by Ajax. He eventually left Ajax for first-team football in 2016 when he joined Almere City and this summer he completed the circle with a free transfer back to Volendam.
