Considering the popularity of my piece ‘how to create a squad profile’, I thought it would be a good decision to highlight how data and Tableau can be utilised to provide additional information to aid recruitment.
The Data driven approach to player recruitment is certainly an area of the game which has seen a significant growth over the last decade, something which I discovered when reading Football Hackers by Cristoph Biermann.
The book touches on how Matthew Benham, a former professional gambler and now owner of FC Midtjylland uses data from his company Smartodds to analyse players across Europe, through assessing their outputs in relation to key metrics to pinpoint prospects who may have been undervalued because their underlying statistics suggested they were better than they appeared.
The outcome Midtjylland, a Danish club that spiralled downwards in the Danish leagues climbed the ladder to secure three Danish Championships.
Harvesting the data
As per my guide (link above) the data is collected in the same manner, however as previously covered it is important to ensure that the view on Football Manager has all the relevant data you want to study.
I have created a custom view which can be used to capture the raw data, the link is at the end of this article which can be used to save time manually sorting/customising your screen.
Why per 90? Simple, by looking at metrics alone you can often be misled by the output figures e.g. if player A has scored 12 goals and player B six, you would automatically presume player A is the better finisher.
Now if you take minutes played into the equation, player a has played 1000 minutes whilst player B has played 400 their per 90 metrics look like this.
Player A 1.08 goals per 90 and player B 1.35 goals per 90, making player B the more attractive option. As you can see using the p90 metric r90. It gives us context when evaluating players who play wildly different minutes over the course of a season.
The search for a creative CM for OGC Nice
The below image shows my best eleven from across the season in relation to their average ratings. I have included their per 90 metrics to provide context in relation to their outputs/contributions for the club over the season.
The above data for my midfield unit for the 2020/21 season has Rony Lopes (IF) as my most creative player, contributing with 3.91 key passes per 90.
My best central players are all performing under 2 key passes per game, this is not ideal. In order to transition to playing the Sistema, I will need to secure a player who can operate centrally and create, so think either an Advanced Playmaker or Mezzala.
I need a player who can create chances for OGC Nice, someone who can work his magic in tight spaces with the ability to complete passes in a compact defensive structure where you would assume no passing lanes were open, a magician.
Unfortunately we have another problem, Pierre Lees-Melou has the highest xG per 90 of the midfield trio, this figure sits at 0.19. His actual goals per 90 figure is 0.08 meaning our biggest goal scoring threat centrally is underperforming by 0.11. This over the course of a season (38 games) means that he should be scoring just under 1 in 5 games but has performed at 1 every 10, so we also need someone who can provide a goal scoring threat.
Therefore I will use data to aid provide additional information in order to effectively target my next signing for the club. I have opted to select players from Ligue 1 who operate centrally to find my target, all must have played over 1,000 minutes.
I have grouped my players on each data viz across this post and highlighted them red in order to show how they are performing against similar players.
Alexander Golovin (25) from AS Monaco is the volume shooter of Ligue 1, whilst Dimitri Payet (34) Marseille has the highest xG value which means credits his ability to get into positions with a high chance of scoring.
The three players which caught my eye using this cut of data were, Benjamin Bourigeaud (27) of Stade Rennais, Ryad Boudebouz (31) of AS Saint-Etienne and Ludovic Blas (23) of FC Nantes.
All three players are taking more shots than average whilst producing an xG which is significantly higher than the average, none of which play for so called big clubs which would mean they should have a lower value.
The next viz compares passes completed with key passes, at OGC Nice we play a short passing game therefore would need to recruit someone who is comfortable receiving and completing many passes per 90. The objective to find someone who can create shooting opportunities centrally mean that the key passes metric is also valuable.
Abdoulaye Toure (27) of FC Nantes is completing the most passes per 90 in Ligue 1 whilst Alexandr Golovin again shows his value to AS Monaco by completing the most key passes per 90.
Yacine Adli (20) from Bordeaux, Mickael Cuisance (21) of Marseille and Bruno Guimaraes (23) from Lyon are all players which show they are performing above average on both metrics and could be considered an option.
Ludovic Blas, is borderline in relation to key passes but easily performing above average in relation to passes completed.
Finally we look into interceptions per 90 as there is still the need for our target to good anticipation, decisions etc in order to read the play and win the ball back, hopefully in advanced positions in order to put us on the front foot as we operate with a higher defensive line.
Benjamin Andre (30) playing his trade with Lille, Youssef Bennasser (24) from AS Monaco and Ludovic Blas would make the shortlist in terms of this metric. It is also nice to see Khephren Thuram close to this group as I can get a mental comparison as to what these players are offering their teams.
Now that you have conducted the relevant data analysis it is time to send you best scout in terms of judging potential ability and current ability off to scout the individuals.
The man for the job my end is Abel Almada, the Argentinian has 18 for both attributes and is my main scout for giving the final verdict for players highlighted from my scouting pool.
Youssef Bennasser at the point of conducting my analysis had ran out his contract with AS Monaco and will be joining Lyon as of next season, this gives a flavour of the calibre of player we have identified.
Yacine Adli scout summary returned with a score of 81 making him a quality acquisition for the club, his DNA profile just fell short of the requirements which is >12 for Anticipation (11), Composure (14), Concentration (11), Decisions (14), Flair (15), Teamwork (12) and Vision (17).
One key point to highlight is that his club do not want to sell the player, this could lead to us having to offer a significant fee in order to secure the players services.
Ludovic Blas report come back from Almada with a score of 82, again a quality signing for the club and the estimated cost of £11m-£15.25m also would hopefully mean a significant return for not exactly a mega investment.
When considering Blas’ mental attributes to see if he would fit with the DNA which I am looking to implement at Nice, he scored;
Anticipation (14), Composure (14), Concentration (14), Decisions (14), Flair (14), Teamwork (13) and Vision (15)
The 23 year old hit all of our desired criteria making him our final choice and our number one priority target.
I truly hope you enjoyed this post, as you can tell I clearly enjoy data and the added value it can bring to your saves. I have also added links to the images, these can be accessed by clicking on each title.
If you would like to get your hands on the custom view which I use to export the raw data this can be found here on my Steam Account.
RT’s will be massively appreciated along with likes of this post (claps) I believe they are called on Medium.
Until the next time!