The Background
There’s a lot of dubiety about what ‘moneyball’ actually means. Yes, it was a great baseball movie based on a book, with Brad Pitt in it. No, it doesn’t mean you need to bin off every other piece of information you have access to and make decisions solely based on individual numbers in isolation.
To quote a 2020 article on Birmingham Live about Aston Villa, effectively “Moneyball has become a term used to describe a data-driven approach to recruitment and team decisions on the transfer market, leading to improved on-field results.”
That sums it up nicely. It’s using data to gain an edge or uncover talents that may not appear to have significant value at first glance, so others may overlook them.
I’ve written about my data-driven approach to recruitment many times before, most recently here, where I wrote about my feelings around statistics in football analysis and in Football Manager, and then introduced some new metrics (SE, CE, DI etc) that I like to use to compare players.
Today I am focused on recruitment, so let’s look at using this method to identify high-performing, value for money prospects out in the transfer market in Football Manager 2022.
I said I’d be back with a downloadable spreadsheet (the Player Search Tool) and a guide on how to use it, so here we are.
The Download and Setup
There are just two things you need: the custom view for Player Search and a copy of the spreadsheet that automatically does the calculations for you (more or less). This is called the Player Search Tool.
Both are available (for free obviously) when you click the link below. Download from MediaFire and unzip the file into those two items.
Drop the custom view (FM Stag – Player Search.fmf) into your views folder, which will be somewhere like Documents/Sports Interactive/Football Manager 2022/views. Then in-game, go to Scouting > Players > Player Search > Custom > Import View, like below, and select the Player Search view you’ve just downloaded.
Save the downloaded Excel sheet (FM Stag – Player Search Tool.xlsx) wherever you fancy, then open it up and it will look something like this, depending on your monitor size and resolution. You might need to scroll left and right to see all the columns if you are using a smaller screen or don’t have binoculars to hand, and that’s ok.
That’s you set!
Time to jump into FM22, run an appropriate Player Search and take it from there.
The Player Search
Say your side are lacking regular goals and you are looking for an appropriate number nine to lead the line in attack. Who isn’t?!
Now you could simply run a Player Search, filtering results to strikers with an appropriate reputational level and having scored a minimum of x amount of goals, but what if you want to see past the obvious and scout a little deeper? This method won’t get you all of the data you need.
Though the World Reputation method is quick and easy; if you are using a data-driven approach to uncover hidden gems, the depth of results rather than speed you can find a transfer target is more valuable.
Also, when using data in FM, it’s crucial that you only consider players who are playing in leagues which are loaded in full detail in your save. This is because there is a known discrepancy between the data output from leagues loaded in full detail versus those which are not. It makes sense, fully loaded playable leagues have all the in-depth detail. Data can only be truly useful if it is consistent.
Therefore, when running your search when wanting to use data-driven recruitment, always filter your results to only show players from teams in leagues which are loaded in full detail, i.e. those appearing in Add/Remove Leagues which are listed as ‘Playable,’ as per below.
Since I’m managing in Chile, from the playable leagues in my save, it felt right to look for targets who play in the top tier in either Chile, Argentina, Colombia or Uruguay. Pick target leagues for your own save accordingly.
Now my Player Search results screen looks like this…
The Method
Now we are ready to export some data from our results and import them into the Player Search Tool.
First, sort the results by the metric you consider loosely most important for this search. For this one, I’m going to click ‘Goals.’ If it was a hunt for a dominant defender I may have clicked ‘Headers won per 90.’ For a playmaker perhaps ‘Key passes per 90.’
Next, click to select the very top result, then press Ctrl + A then Ctrl + P (or the Mac equivalent). It’s crucial not to scroll or click anywhere else at this point. Just click the top result, then Ctrl + A then Ctrl + P. Your screen should look like this…
Now click ‘Ok’ and save the ‘web page’ wherever makes the most sense for you. I’ve got a folder called ‘exports’ in my documents that I just use for that. Call the file something like “scouting striking targets” or something memorable. It will save as a .html file that will merely open in your internet browser if you double click on it rather than doing the next step.
Next, open up Excel and go to File > Open and open the html file you just saved from FM.
When you open it, it will look something like this…
Next we need to grab the data we need from this sheet, in order to paste it into the Player Search Tool.
Click cell C2 (the position of the first player on the list) and drag all the way to the right to include even column AB for Wage. Also drag down as far as you wish down through the data. The Player Search Tool limits at 189 rows / players, so you can include as many rows as if you wish, up to 189 players; if there are that many rows of data.
Press Ctrl + C to copy this data to your clipboard.
Next, separately but simultaneously have another instance of Excel opened with the Player Search Tool from the earlier download onscreen. Right click on cell B3 in the Player Search Tool and then select the paste option with the ‘123’ symbol. This is crucial, as it will be pasting the values only from the other Excel sheet, not the formatting and font sizes etc. It has to paste the values only, which is the ‘123’ icon here.
The Player Search Tool will now look similar to this…
We are nearly there.
You will notice that there are a lot of dashes – where there should be zeroes 0, and that’s what’s making all the formulas on the right serve up errors like #VALUE!
Therefore, you need to select the area with the darker blue background (rows D to AA and down to the bottom of the list of players) then click Find & Select and replace any dashes – with a zero 0 using the Replace All button. The screenshot below explains this part.
Next, to ensure the value calculations work properly, highlight the entire contents of column AA, ‘Salary p/w’ and use Find & Select > Replace All twice in a row:
- Replace the ‘£’ symbol with literally a blank space (hit the space bar in the ‘Replace with’ field).
- Then replace ‘ p/w’ with another blank space only (same trick).
After you hit Replace All the second time, you’ll notice that all the money-related columns with a green background on the right hand side now work properly.
NOTE Anybody in the results who now continues to have a #DIV/O! error in any of the fields on the right with the green background, just means that they literally had zero actions for one of the key metrics for that calculation. I.E. We are looking at strikers here and if a player has an error where his Creative Efficiency score should be, it means he literally hasn't made a single key pass or created a single chance, meaning he literally has no score for that metric. You may notice something similar if scouting for defenders and using this tool, primarily comparing Defensive Interferences. Some of the players in the list may have an error for Striking Efficiency, which will mean they have literally not attempted a single shot, even though they've completed lots of other actions on the pitch. You can consider these fields to have a zero in them.
Now the tidiest thing to do next is just delete all the rows after your last player on the list, in this example meaning deleting all of the rows 39 and below. The quickest way to do this is to click on the first empty row on the left then press Ctrl + Shift + down arrow keys together (this selects every row all the way to the bottom of the sheet) then right-click and delete. Click ‘Ok’ if a prompt tells you it will take ages. It won’t.
Voilà! Now you have your Player Search results onscreen together, all with specific values pre-calculated for their Striking Efficiency, Creative Efficiency, Defensive Interferences, Overall Attacking Contributions, Overall Defensive Contributions and those last two combined. Plus all the comparable values based on their salary.
I explained all of these new metrics I’ve put together in the other post, but if you haven’t seen it, the general explanations are as follows:
Striking Efficiency (SE) – A player’s non-penalty goals per 90 minutes divided by his total non-penalty shots plus his non-penalty xG (Expected Goals) per shot. Basically how efficient a player is at finding the back of the net in comparison to how many shots he attempts, but removing penalties from the calculation and including an element of how good his movement and anticipation is (higher npxG means he got into more positions of higher goal-scoring potential in open-play).
Creative Efficiency (CE) – A player’s chances created per 90 divided by his completed passes per 90. This is how many times per x amount of passes does the player play a pass where the receiver takes a shot at goal from a position of 0.15xG (a “half-chance” in FM terms) or higher. A way of understanding if the player is a regular playmaker.
Defensive Interferences (DI) – A player’s combined interceptions, clearances and successful tackles per 90. I’d like to include blocks in this calculation, but unfortunately getting the blocks statistic into the search and squad views isn’t possible in 22. Basically how effective a stopper the player is at denying opposition chances.
Overall Attacking Contribution (OAC) – A player’s combined key passes, chances created, successful dribbles, shots on target, goals scored and non-penalty xG per 90, plus his non-penalty xG per shot and an adjusted average rating. A combined number denoting overall offensive effectiveness.
Overall Defensive Contribution (ODC) – A player’s combined interceptions, clearances, successful tackles and headers won per 90, plus an adjusted average rating. A combined number denoting overall defensive effectiveness.
OAC + ODC – Like it sounds. A combination of both of the above metrics to determine a player’s “all-round” effectiveness. Probably best to use for comparing central midfielders or full-backs who you may require to be active and competent at both ends of the pitch, and not solely in just the attacking or defensive discipline or phase.
What you do next is entirely up to you. Right click on any header and sort Z-A for the largest numbers at the top to rank the results from the “best” player in that category down to the worst. Then as per my previous post, you could then consider the value/cost of these contributions, ranking them A-Z for the lowest numbers at the top for the ‘cost per’ columns.
Why not try and put together the most cost-effective team, grabbing the highest-performing players from each category from the country you are playing in but with the lowest cost base? Effectively buying defensive and offensive actions and getting on-base. I mean, buying wins. How very Moneyball of you…
In Action
Do you always go for the players who appears in the list with the highest statistical outputs in order to buy the highest performers, regardless of cost? Do you instead always go for the players who are good enough to appear in the list in the first place, yet have the lowest cost per action for one of those key metrics in order to save the bank balance while still adding quality? What about weighing up the performances vs the salary cost in a delicately balanced trade-off between the two? It’s entirely up to you.
In my example sheet, below, the result seems a no brainer when looking for a striker. Take a look…
Renzo López has the highest Striking Efficiency (SE) score of the whole list, with a score of 0.848. If you look one column to the right, you’ll also notice that his ‘Cost per 1SE’ is the lowest in the list too. So not only is he the most effective attacker when it comes to the SE score (a combination of non-penalty goals per 90 divided by his non-penalty shots plus his non-penalty xG per shot), but he also represents the cheapest cost and therefore the biggest value for money when you consider his performance vs his salary at his current club. Win-win!
Now there are many possibilities with what you do next. Do I consider it job done, tab back to FM, bid for López and move on? Or do I look a little deeper again?
Third on the list is Lionel Altamirano, and not only is his Striking Efficiency not quite as high as López‘, his wages are also a lot higher, so his perceived value for money is nowhere near as strong either (much higher cost per 1SE) when solely considering these striking metrics.
BUT, if you look along to the other columns, Altamiro actually has a higher score than López for Creative Efficiency, for Defensive Interferences and actually for Overall Attacking Contribution and Overall Defensive Contribution too. So it all depends on what I’m looking for. Do I want my new number 9 to be a pure and efficient goal-scorer only? If so, López is an absolute no-brainer as the best choice from the list. Am I instead looking for a more well-rounded forward who will get involved in build-up play and even contribute defensively? Maybe the extra cost and marginally less Striking Efficiency is a fair trade-off for a more complete footballer?
It all depends on my tactical approach and what I’m looking for here. In my current system I need a pure poacher and our team is pretty low on funds, so for me, López is the hidden gem I’ve uncovered here, and the player I’m most keen on.
At the very least these considerations bring new facts about players to light, ahead of scouting them in-game and considering their attributes, injury history, age and all the usual factors.
It’s all about gathering as much data and insight as possible when scouting so that your decisions are informed and based on as many factors as possible.
Conclusion
My goal here across the two articles was to explain my feelings about performance statistics in football and in Football Manager, introduce some joined-up metrics I like to use to evaluate and compare player performance and to make the sheet downloadable and my methods explained in steps.
It might be an automated calculator, but don’t let performance numbers be your sole driver when out there scouting. It’s just another tool in your toolkit when analysing potential targets and comparing the performance of players.
What you do with the data and how you use it to help with your decision-making when exploring the transfer market is entirely up to you, this is just another potential string to your bow.
You are the manager, after all!
I plan on other articles like this in future, including a Goalkeeping Performance Calculator among other ideas, so keep an eye on my Twitter and website, if this is your kind of thing.
Thanks for reading.
FM Stag