Screenflow Scouting Spreadsheet

This is an old post but still relevant today.

So, it’s late in the FM23 cycle, FM24 has been announced, you’ve got a banger of a save going on (In my case BFC Dynamo on the blog), and you are using Screenflow Scouting to help you.  But there’s a problem.  Once you are past the ‘Great’ countries to scout and look for, where do you go next?  What competitions do you prioritize, what teams in those competitions do you prioritize, and how the heck do you figure that out?

The answer?

ChatGPT, PyCharm, and a ton of patience.  I am not a coder.  I’m just not.  But after two weeks of asking questions, running tests, and a decent amount of cursing and redo’s, I managed to put together a program that scraped several online FM22 and FM23 databases and wrote the data to an excel spreadsheet.

When it finished, it looked like this:

Or are they?  What can we use to really determine that?  Well, each team is scored/graded on the same Four Conditions:

Training Facilities

Youth Facilities

Youth Coaching, or the Quality of Coaches using those Facilities

Youth Recruitment, or the reach of your club in finding players

And, believe it or not, SI has a chart they use on how to determine the numeric value of such things.  And in true SI fashion, it is entirely inconsistent with naming conventions and ranges.

To be honest, while I do see some of the reasoning behind this, it still does make me grind my teeth from time to time.

So, how do we use this to determine a teams score for a particular area.  Math and a VLOOKUP chart.

We take the average of each and go from there.  Does this help ‘bad’ teams and hurt ‘Good’ teams?  A little, but not so much it matters IMO.

So, using Training Facilities as an example:
State of the Art = 20

Superb = 18.5

Excellent = 16.5

And so on.  Get that sorted, another excel formula later, and you get this:

Add those 4 numbers together, divide by 4, and you get an average, like this:

And then you add a ‘Score’ Column, and you tell the spreadsheet to find all the teams who have an average score of ‘9.5’ or better, and assign the number 1 to the column, like so:

Then, after hiding some columns and filtering out for the 1, you get1584 teams that have a combined score of 9.5 or better.

And then the fun begins!

Because you now realize, about a year after the fact, you fatfingered a key and instead of “Training Facilities – Average” being 10.5 you have it as 0.5.  So your missing a few teams as a result.  About 800ish…

Blivet.

Four hours and 120ish tabs later, all the pages have been updated.  So, whee we?  Ah, yes, sorting those teams out.

But first, let’s get some other Scouting Concerns out of the way, because that’s what I created this spreadsheet for in the first place, the help me with Scouting.

When it comes to which countries to scout, were do you go?  The countries with the best Reputation and Youth Rating.  That’s 12 Countries.  Now what?  Where do you guy to find those hidden talents before anyone else.  Well, using Game Reputation, Game Importance, and Youth Rating, you can divide all the Countries up into Tiers.  

I did it by looking at those categories, then using Game Importance as a factor, with some personal judgment calls, we get the Tier List:

Honestly, if people want to quibble over some of these, that fine.  🙂

With regards to scouting, where do we allocate sources if were willing to skip the S Tier countries for the time being?  Well, if you were to sort the A and B Tier countries by Region:

In the ‘A Tier’, a Recruitment Focus with a Scout hitting Ivory Coast, Ghana, Nigeria and maybe Cameroon would (until the Dec update anyways) provide you a list of good, cheap youngsters with a lot of potential.  Like wise, in the ‘B-Tier’, a Middle East Recruitment Focus, and a North Africa Recruitment Focus that combines the Countries in the A and B Tier would return some good results.

But what if you aren’t happy with your Recruitment focus results, and are using Screenflow Scouting?

For that, we have to go a bit more granular.

Several Screenflow Competitions will list the teams in that competition.  For An International Comp, it will be the National Teams.  For Continental Competitions, say something like the African Champions League Qualifying,  you go to Team Detailed, and you see this:

Do we want to click on all the teams in the competition and work our way down?  Maybe.  In this case there’s 64 teams.  Some competitions have 100+.

So, this is where the rest of the Spreadsheet comes in.  Click on the Team, it will take you to the Team page, and you will see AS Maniema Union is in the DR Congo.

Go to the Africa Tab, and if the Nation you are looking at has at least ONE team with a rating of 9.5 or better, it will be and underlined, in document link.  

Click the ‘DR of Congo’ link,  and you will see

We will see US Maniema Union didn’t make the cut.  It’s a judgment call then, to click on them and add the roster to your Cull List.  And that’s not to say those countries without tabs won’t produce a good player every now and then, they will.  But IMO, the odds of finding a good young player are better when looking at clubs in countries that will produce them, as an example, in Ivory Coast.

And after awhile (in my case a few years) I know that if I see a kid from screenflow pop up, and he’s from ASEC Mimosas, I am more likely to spend the scouting fee to scout him than I am if he were at Maniema.  Could I be missing out?  Possibly, but the odds are against it IMO.

This method comes in quite handy in some of the ‘Smaller Competitions’, such as the Caribbean Cup, and those Qualifying Tournaments where teams from ‘Smaller Nations’, your Central American Countries as an example, compete.  As an example, my knowledge of South American Football clubs is at best a Power Point Slide deep.  I’m looking at a snapshot from a young player from Ecuador, he looks decent, chances are a lot higher I’ll pay the fee to scout him if he’s from El Nacional than I will if he’s from Espoli.

Now, a couple of notes on some of the tabs:

If a team has a 0 in it’s Team CA or Team PA, it is either an Academy Squad, a B team, or the database did not have the info when I scraped it.  This is FM23 data mostly, so the Japan information may be off the most.  Divisions may not match, as we are a year plus into the seasons since I compiled this.  

It’s also possible, likely even, that many clubs have had improvements, or downgrades.  I am not to worried about that, as this is a baseline.  If a Team is Really, Really Good in one or two areas, but horrible in the others, there’s something else going on.  Looking at you Burton, Young Violets, and Loughborough University…

For many teams in “Smaller” Nations that made the cut, what will put them over that 9.5 threshold is their Academy Coaching and Youth Recruitment.  

The spreadsheet can be downloaded here:

LINK

Any questions hit me up on Twitter @FM_Jellico

And for those of you thinking I might have missed a small country or two, you may be right.  See tab 131.

Thanks for Reading!