Recruiting Strikers From The Second Tier Divisions – Data Analysis

This is the final instalment of a three-part data analysis series that I have been writing this week concentrating on using data to identify potential strikers in the second tiers of the top 5 leagues.

In the first part of the series, we were looking at potential targets in the centre of defence.

That was followed by the second part in which we looked for a new central midfielder with a set profile to search for.

The premise behind the series is simple, we have joined a new club as a recruitment analyst and we have been immediately set a task to identify targets for the upcoming transfer window.

In order to achieve this, we are, in the first instance, using raw data to identify players that are performing well this season.

This is not a means to produce a final list of recruitment target but rather to take a large set of players and filter it down to a more manageable shortlist that we can then drill down into using video and live scouting that go more into the tactics around each player.

We have applied certain filters to the data in order to concentrate our search.

The first is obviously to only consider players in the second tier of England, France, Italy, Spain and Germany.

We then filter the results to players who are under the age of 23 and who have played at least 1500 minutes so far this season.

In terms of the player profile that we are looking for, it is reasonably simple.

We want a player for the ‘9’ position who is capable of playing as a lone forward.

At the end of this data analysis, we plan to have a shortlist of five players that can be put forward for further checking.

Recruitment First Steps

The initial step to the process is to find and export the data that we want from Wyscout.

This data is then collated into a single spreadsheet with all of the players that we are potentially interested in.

Then, that is imported into the public version of Tableau and we can use the tools available there to produce visualisations that present our data.

You can find the result below with a scatter graph.

xG per 90 Vs Shots Per 90

The first check that we run is a relatively simple one.

We are comparing a player’s expected goals (xG) per 90 and their shots per 90.