I created a few generic "standards" - all measured in an XLS using ykykyk weights for each position.
What I do is enter in the player score for the attributes identified in ykykyk.
Then I multiply that player's score against the weight for the attribute. So if Pace for a position is a .8 weight, and the player is a "10" - they are given an 8.
I then add them all up into a aggregate - Total Weighted Player Score. TWPS becomes the basis for other measurements/comparisons.
As an example, in my current SGE save, N'Dicka has a TWPS of 104.3 as a DC. Baidoo has a TWPS of 93.4 at the same position. Hardley an 86.8.
Next is "weighted player score above average" - where I compare the player in position to a generic "all 12s" player score. Technically, I guess "10" might be average, but I use 12 as my baseline.
So N'Dicka has a WPSAA of 11.9, Baidoo is 1, and poor Hardley is -5.6. N'Dicka is above average, Baidoo is about average, and Hardley, well, I hope I can loan him to a Bundasliga 2 side for his cost in salary.
I've also been playing Bromley from National to (just today) Premier for the 28/29 season. WPSAA has been useful in that journey. At National I was shooting for a WPSAA of -3 for each position in my starting 11. In Championship - +4 to +7 helped me get the highest xG and expected points in the league (Fulham overachieved and took top spot).
The other score I use is % of "Best In World" - when the top 50 in the world reports come out - I find the players with the top TWPS for each position. I then compare my player against the BIW. A simple %. It's tougher to upkeep this stat, and it is variable from save to save. But it can be nice to know how far a 22 year old Junior Kroupi is from an aged Mbappe.
Final use for TWPS is something I'm playing around with called "Positional Difficulty." An attempt to measure market scarcity - "PD" measures the TWPS for Best In World for the position against a TWPS of all "20's."
The theory being that the greater the gap between all 20 and BIW - or the higher the PD score - the more difficult it is to find high TWPS scoring players for that position. This could be total nonsense, of course, but I use it prioritize spending. DR and DM positions have had higher PD scores in a few saves I have - and based on playing style, I tend to spend more aggressively for those positions.
I appreciate the work in Python. For those asking "why don't" - well, some of us do a bit more. I have my own ratings (customized to my passing-heavy style of play), my own metrics (ratings against "average" or "best in the world" ) and my own views (what is my best squad for national competitions?).
Diversity of thought and effort is pretty good. But I'm sure if you just use Squirrel Plays or whatever, you'll be fine too
@skawkclsrn - GA/GS difference testing comes from the attribute testing on this site. It’s specious for some of the reasons @MeanOnSunday mentions. But I think it’s somewhat meaningful in that we’re looking not just at the inputs into the simulator (as ykykyk or Mark’s or SIs or whomever) but shows us how the simulator responds. It allows us to ask ourselves questions like, “if ‘Off the Ball’ isn’t meaningful in a controlled environment - how could it be meaningful in my environment - (a 4-2-4 in my case)?”
First, let me be the first to say I’m not good at analytics, soccer (I’m just a Yank who became interested in it at the tail end of COVID), nor FootballManager (I’m only on my 5th career here).
So negative feedback is EXACTLY what I’m looking for. I hadn’t thought about the correlation and additive nature of calculating.
RE: positional differences being averaged out - wouldn’t that also be a potential benefit? Going into this analysis, I thought about:
1) the fact that it’s not my tactic, and 2) it’s coarse on positional differences, and 3) it’s older (FM22), and 4) it doesn’t test all attributes, and 5) some of the differences may not be statistically significant (+2 goals over 913 simulations? Meh?).
So the compromise I’m offering is not weighting every attribute, but simply trying to combine those that the simulation seems to respond to, and then adding enough attributes that are NOT measured in the attribute testing with ykykyk weights.
Finally, I’m starting to think that any of these approaches are “good enough to be good enough.” Using @Mark weights - I’m dominating MLS on the Touch version. Like no scum save just went a season without losing. Would it even matter if I switched to ykykyk? Is my success because I “cheated” came onto this site, and emulated 4-2-4 Bombyte Tweak more than finding really great players?
I mean, I had success in Ligue 1 with Lorient (!) just measuring how many attributes weighted above 50 were above 16 for each player. Not nearly as granular as any of the above - but still successful.
As long as I know that P/A rule, and the stars are a lie, and I’m playing a proven tactic - is the whole XLS exercise just there for me to not ever overpay for Xavi Simmons again?
They both operate under the assumption that the information provided by Sports Interactive (SI) within the game is entirely accurate. Per position attributes are divided into three categories: Required, Preferred, and Irrelevant. Required attributes carry the most weight, preferred attributes a bit less, and irrelevant attributes are ignored in their calculations.
Right? That's one of the reasons I love the attribute testing on this site, but as you say, it could use what feels like an endless amount of work and variable manipulation to get closer to reality.
I love the python tooling. Will switch someday to it - if only because I want to weight statistical results in decision making more than the "attribute weight only" approach I have now.
This is probably one of the best marketing campaigns ever.. agree, and esp. if you want to get on the radar of enthusiasts.
My guess also (given Bromeley's stature) is that they want a better analytical program, but don't have the money to do more than find someone who does this in their spare time, hates their low-paying job, and will give them heart and soul for little compensation.
Hell, as a yank, I'll do it for free, part-time, if they pay for my expenses...
There's an appeal to me to make my experience a little less "reverse engineering the simulator" at this point. That said, I've thought about:
1) Playing on PC and getting a skin that blocks the attributes altogether. The issue with that approach is that I feel like if I were, say, actually taking the Bromely job, there would be some subjective measurements for things like "determination" or "composure" and objective measurements for things like pace and acceleration. So....
2) There are skins and whatnot that give ranges for the attributes on the PC I'm lead to understand - which might be closer to how an analytics department might work.
I thought I'd reach out here and see if anyone plays without the precise (or even showing) attibutes, why, what your set-up is like, etc.
I just type them in. Again, I've been playing on an iPad, and with it's limited capabilities, I will just type stuff from screen to screen. I suppose I could create a custom view based on the attributes for the individual position, but i'm lazy.
ABOUT THE JOB We are looking for a talented Football Manager™ gamer who wants to test their skills in the real world and take their first step in a potential career in professional football. This person will join Bromley’s tactical staff for the rest of the season. No CV or experience required. This is a backdoor to the backroom for someone who wants to explore a career at a professional club. We want a Football Manager™ expert, the guru of the group chat, the final score forecaster. In this role you will not have to make any executive decisions, but you’ll be shadowing and learning directly from all backroom positions to understand the ins and outs of managing a real football club.
KEY ROLES AND RESPONSIBILITIES Contribute to all 1st team analysis, pre, during and post-match, live training and notational analysis. Support with analysis, evaluation and feedback for 1st Learn to produce detailed 1st team opposition analysis, using subjective and objective data in line with the Club’s philosophy and expectations. Tap into your Football Manager™ knowledge to support the management and backroom team on all areas including team selection, tactics and scouting. Present detailed reports and feedback to the 1st Team Manager and key staff. Develop and maintain performance related databases for analytical reviews. Assist in target setting and developing individual player performance plans. Perform additional duties as required to meet the needs of the Club. Stream and vlog about your experience with Bromley FC in-line with the agreed terms and conditions.
Because I just have my little XLS - the depth of analysis you're putting in seems pretty cool. I started playing this game to learn more about soccer (yank here). Now I'm enjoying the ability to analyze the "game within the game" - and with 15 XLS and so many formulas - I feel like it's great to have a community of analytical minds.
I'm toying with a new rating system, and would love some feedback. It ignores GK - but bear with me on that. I think a different approach is warranted for that special position.
First question is - how much do we believe the underlying simulation algorithms change from year to year? Specifically,
Do we suppose that these would be meaningful in 24?
If so, then I like these more than weights, because it shows how the simulator responds to inputs - it's more "evidence-based."
So I did a little analysis last night, and compared the Attribute Testing results to ykykyk, in an effort to create a new roster prioritization / scoring approach that relies as much as possible on the Attribute Testing results.
Methodology -
1) Looked at the measured attributes and segmented them into whether there was a defensive impact, an offensive impact, or both.
Looking specfically at Goals Allowed (Defense) vs. Goals Scored (Attacking).
2) Next, created a basic Attack template for attacking heavy roles, and a Defense template for Defensive heavy roles.
The different shades of green are used to help me identify if the impact of the attribute was pretty strong, medium, or weak.
I then reckoned that I would limit the number of attributes I measured based on roughly 50% of total attributes available (so 18 attributes to be considered). That meant that for attackers - there were only 14 relatively meaningful attributes, and for defenders - 16.
4) I decided to "fill in the missing attributes" by comparing the Attribute Testing with ykykyk weights for each position. So, Finishing isn't represented in the Attribute testing, but sure seems important to attackers in terms of weights, so I added that.
5) This all resulted in the following:
So you see the attributes, the numbers in the first row represent the ykykyk weights, the second row numbers are the impact on Goals Allowed difference or Goals Scored difference.
The red numbers represent extrapolation (complete hogwash, just interested in looking at unmeasured attributes i added, and if the attribute wasn't measured, what is the possible impact largest impact based on attributes with similar ykykyk weights). I pretty much know the red numbers are hogwash because Off The Ball is weighted highly in some positions, but with no demonstrable impact in the attribute testing results.
From here, I would simply replace my current approach (weights-only used in ranking, with this new idea.
I want to measure a couple of things depending on the state of the club I'm managing.
What I do is try to come up with an aggregate rating - that may be just entering key attributes against ykykyk or somesuch. That's useful. But then I do two things -
1) Compare my player aggregate rating to a player with "all 12s" - in an attempt to recreate something like "Wins Above Replacement" as used in baseball. Basically - is my player better than the average flotsam I can pick up easily - and how much better? This helps in roster construction and transfer window opportunities, and esp if I'm playing a small or poor club (or the MLS).
2) Every time the game posts "Top 50 Best in World" to my inbox - I go and find the highest rated player in that top 50 and put them in the same ratings as I use for my players. Then I create a comparison metric that answers the question "how far away is my player from the best on the market?"
This is useful for clubs that are rich and / or when I want to dethrone PSG.
3) I'm playing with the idea of comparing my player's ability vs. how much I'm spending. I haven't come up with a good approach other than to divide the aggregate rating by salary here, which can be helpful, but I feel like there might be a better approach.
Finally, I'm playing with the idea of using/stressing the attribute testing on this forum:
Which I *love* because it shows me based on results vs. the weights. I feel like measuring based on how the simulators algorithms respond is more meaningful than just how it constructs the inputs into the simulator.
Why you don't use FMRTE? I think its more advanced scouting tool and also with much more flexibility on ratings etc. I was using Genie Scout too before some years but the developer of the app don’t update it anymore with new features, only fixing some bugs and make it work for each edition/patch.
Thank you again for your time and your effort you put. Keep going Expand
A couple of reasons:
First, I play on an iPad. So it’s easier for me to just use an XLS (which I really only use 3-4 times a season.
Second, I have custom metrics that help me evaluate a player. So if I decide to switch to PC or Mac, if FMRTE allows me to do custom analytics - that might be an attractive option.
Third, when I play with the numbers myself - I learn more about the game maybe?
This suggests that Jumping Reach, Concentration and Strength, are key “defensive position heavy weight” is a key defensive performance attribute.
Balance and Finishing are big “offensive position heavy weight” and key offensive performance attributes.
So maybe we’re looking at 3 Tiers of interesting attributes -
Tier 1 - ykykyk down to, say, Aggression (or Decisions, if you’d like) PLUS for offense Balance and Finishing, and then for defense, Jumping Reach, Concentration, Strength.
Did some analysis on ykykyk weights vs. the FM Arena attribute testing. Sorry if this has been discussed before and I missed it...
Basically, I wanted to compare the two, so I aggregated the value of each attribute in YKYKYK (with and without GK position). Then created an average for the attribute across all positions. You'll see that in the first two columns, sorted from highest to lowest. P/A are top, leadership on the bottom.
Then took the attribute testing from this site, and ranked that from most impact to least (and made a little %). Here's the results.
First, I want to say that if you play on PC and are good with Python, you should really follow what Squirrel_Plays is doing on Youtube. It's amazing, it's the right way to go about stuff like this, total option for automation and quantitative analysis.
However, if you play on Touch/Console or just aren't that great at Python (I fit both categories) you might want a spreadsheet tool to help with player evaluations and transfer windows and such.
It uses FMScout attribute weights for positions vs. ykykyk, but you could easily modify it to use ykykyk. (I also use a different modified version for my saves that stresses other attributes and gives different weights to attributes that I would stress for my style of play).
But here's some things you might find helpful:
1.) You enter in the player attribute numbers, and it creates a score that is the sum of all the attributes multiplied by their FMScout weights.
2.) It calculates a score that compares that aggregate player attribute score to a score of a player who has "all 12s" - trying to represent how the player might compare to an absolutely "average" player (my attempt at baseball's WAR for FManager).
3.) It also has conditional formatting that will show if the player is actually a "13.5" or above.
4.) Because nobody is perfect (no straight 20's) I also do something to compare the player to the "Best In World." Basically, what I do is wait for the game to give me the "top 50" for the year, find the best for each position, and enter their attributes for their position. The weighted aggregate of their attributes for the position are then used as a "Best In World" score and each of your team's players can be compared to the very best. So I have a Wonderkid that I've helped become "95% of an Mbappe" at his peak.
5.) I'm also experimenting with comparing the weighted scores with the player spend (to optimize roster building on poor clubs). But I'm not sold on my current approach (simply dividing the weighted aggregate by their salary).
anyway, if you like it, cool let me know. If there are flaws or improvements, let me know, too.
Generally, budget permitting, I have a primary 11 and secondary 11 squad - and sub in from secondary.
What I do is enter in the player score for the attributes identified in ykykyk.
Then I multiply that player's score against the weight for the attribute. So if Pace for a position is a .8 weight, and the player is a "10" - they are given an 8.
I then add them all up into a aggregate - Total Weighted Player Score. TWPS becomes the basis for other measurements/comparisons.
As an example, in my current SGE save, N'Dicka has a TWPS of 104.3 as a DC. Baidoo has a TWPS of 93.4 at the same position. Hardley an 86.8.
Next is "weighted player score above average" - where I compare the player in position to a generic "all 12s" player score. Technically, I guess "10" might be average, but I use 12 as my baseline.
So N'Dicka has a WPSAA of 11.9, Baidoo is 1, and poor Hardley is -5.6. N'Dicka is above average, Baidoo is about average, and Hardley, well, I hope I can loan him to a Bundasliga 2 side for his cost in salary.
I've also been playing Bromley from National to (just today) Premier for the 28/29 season. WPSAA has been useful in that journey. At National I was shooting for a WPSAA of -3 for each position in my starting 11. In Championship - +4 to +7 helped me get the highest xG and expected points in the league (Fulham overachieved and took top spot).
The other score I use is % of "Best In World" - when the top 50 in the world reports come out - I find the players with the top TWPS for each position. I then compare my player against the BIW. A simple %. It's tougher to upkeep this stat, and it is variable from save to save. But it can be nice to know how far a 22 year old Junior Kroupi is from an aged Mbappe.
Final use for TWPS is something I'm playing around with called "Positional Difficulty." An attempt to measure market scarcity - "PD" measures the TWPS for Best In World for the position against a TWPS of all "20's."
The theory being that the greater the gap between all 20 and BIW - or the higher the PD score - the more difficult it is to find high TWPS scoring players for that position. This could be total nonsense, of course, but I use it prioritize spending. DR and DM positions have had higher PD scores in a few saves I have - and based on playing style, I tend to spend more aggressively for those positions.
Diversity of thought and effort is pretty good. But I'm sure if you just use Squirrel Plays or whatever, you'll be fine too
@skawkclsrn - GA/GS difference testing comes from the attribute testing on this site. It’s specious for some of the reasons @MeanOnSunday mentions. But I think it’s somewhat meaningful in that we’re looking not just at the inputs into the simulator (as ykykyk or Mark’s or SIs or whomever) but shows us how the simulator responds. It allows us to ask ourselves questions like, “if ‘Off the Ball’ isn’t meaningful in a controlled environment - how could it be meaningful in my environment - (a 4-2-4 in my case)?”
@MeanOnSunday
First, let me be the first to say I’m not good at analytics, soccer (I’m just a Yank who became interested in it at the tail end of COVID), nor FootballManager (I’m only on my 5th career here).
So negative feedback is EXACTLY what I’m looking for. I hadn’t thought about the correlation and additive nature of calculating.
RE: positional differences being averaged out - wouldn’t that also be a potential benefit? Going into this analysis, I thought about:
1) the fact that it’s not my tactic, and
2) it’s coarse on positional differences, and
3) it’s older (FM22), and
4) it doesn’t test all attributes, and
5) some of the differences may not be statistically significant (+2 goals over 913 simulations? Meh?).
So the compromise I’m offering is not weighting every attribute, but simply trying to combine those that the simulation seems to respond to, and then adding enough attributes that are NOT measured in the attribute testing with ykykyk weights.
Finally, I’m starting to think that any of these approaches are “good enough to be good enough.” Using @Mark weights - I’m dominating MLS on the Touch version. Like no scum save just went a season without losing. Would it even matter if I switched to ykykyk? Is my success because I “cheated” came onto this site, and emulated 4-2-4 Bombyte Tweak more than finding really great players?
I mean, I had success in Ligue 1 with Lorient (!) just measuring how many attributes weighted above 50 were above 16 for each player. Not nearly as granular as any of the above - but still successful.
As long as I know that P/A rule, and the stars are a lie, and I’m playing a proven tactic - is the whole XLS exercise just there for me to not ever overpay for Xavi Simmons again?
Right? That's one of the reasons I love the attribute testing on this site, but as you say, it could use what feels like an endless amount of work and variable manipulation to get closer to reality.
I love the python tooling. Will switch someday to it - if only because I want to weight statistical results in decision making more than the "attribute weight only" approach I have now.
My guess also (given Bromeley's stature) is that they want a better analytical program, but don't have the money to do more than find someone who does this in their spare time, hates their low-paying job, and will give them heart and soul for little compensation.
Hell, as a yank, I'll do it for free, part-time, if they pay for my expenses...
https://fmstag.com/fm-stags-custom-views-megapack-for-fm24/
There's an appeal to me to make my experience a little less "reverse engineering the simulator" at this point. That said, I've thought about:
1) Playing on PC and getting a skin that blocks the attributes altogether. The issue with that approach is that I feel like if I were, say, actually taking the Bromely job, there would be some subjective measurements for things like "determination" or "composure" and objective measurements for things like pace and acceleration. So....
2) There are skins and whatnot that give ranges for the attributes on the PC I'm lead to understand - which might be closer to how an analytics department might work.
I thought I'd reach out here and see if anyone plays without the precise (or even showing) attibutes, why, what your set-up is like, etc.
Anyone thinking/thought similar?
ABOUT THE JOB
We are looking for a talented Football Manager™ gamer who wants to test their skills in the real world and take their first step in a potential career in professional football. This person will join Bromley’s tactical staff for the rest of the season. No CV or experience required. This is a backdoor to the backroom for someone who wants to explore a career at a professional club. We want a Football Manager™ expert, the guru of the group chat, the final score forecaster. In this role you will not have to make any executive decisions, but you’ll be shadowing and learning directly from all backroom positions to understand the ins and outs of managing a real football club.
KEY ROLES AND RESPONSIBILITIES
Contribute to all 1st team analysis, pre, during and post-match, live training and
notational analysis.
Support with analysis, evaluation and feedback for 1st
Learn to produce detailed 1st team opposition analysis, using subjective and objective data in line with the Club’s philosophy and expectations.
Tap into your Football Manager™ knowledge to support the management and backroom team on all areas including team selection, tactics and scouting.
Present detailed reports and feedback to the 1st Team Manager and key staff.
Develop and maintain performance related databases for analytical reviews.
Assist in target setting and developing individual player performance plans.
Perform additional duties as required to meet the needs of the Club.
Stream and vlog about your experience with Bromley FC in-line with the agreed terms and conditions.
TO APPLY
Show your skills in FM24 by winning a domestic
league to gain the Xbox achievement “Championes”
Visit https://www.xbox.com/en-gb/promotions/the-everyday-tactician and follow the steps.
That's just badass.
First question is - how much do we believe the underlying simulation algorithms change from year to year? Specifically,
https://fm-arena.com/table/13-attribute-testing/#options
Do we suppose that these would be meaningful in 24?
If so, then I like these more than weights, because it shows how the simulator responds to inputs - it's more "evidence-based."
So I did a little analysis last night, and compared the Attribute Testing results to ykykyk, in an effort to create a new roster prioritization / scoring approach that relies as much as possible on the Attribute Testing results.
Methodology -
1) Looked at the measured attributes and segmented them into whether there was a defensive impact, an offensive impact, or both.
Looking specfically at Goals Allowed (Defense) vs. Goals Scored (Attacking).
2) Next, created a basic Attack template for attacking heavy roles, and a Defense template for Defensive heavy roles.
The different shades of green are used to help me identify if the impact of the attribute was pretty strong, medium, or weak.
I then reckoned that I would limit the number of attributes I measured based on roughly 50% of total attributes available (so 18 attributes to be considered). That meant that for attackers - there were only 14 relatively meaningful attributes, and for defenders - 16.
4) I decided to "fill in the missing attributes" by comparing the Attribute Testing with ykykyk weights for each position. So, Finishing isn't represented in the Attribute testing, but sure seems important to attackers in terms of weights, so I added that.
5) This all resulted in the following:
So you see the attributes, the numbers in the first row represent the ykykyk weights, the second row numbers are the impact on Goals Allowed difference or Goals Scored difference.
The red numbers represent extrapolation (complete hogwash, just interested in looking at unmeasured attributes i added, and if the attribute wasn't measured, what is the possible impact largest impact based on attributes with similar ykykyk weights). I pretty much know the red numbers are hogwash because Off The Ball is weighted highly in some positions, but with no demonstrable impact in the attribute testing results.
From here, I would simply replace my current approach (weights-only used in ranking, with this new idea.
Thoughts?
I want to measure a couple of things depending on the state of the club I'm managing.
What I do is try to come up with an aggregate rating - that may be just entering key attributes against ykykyk or somesuch. That's useful. But then I do two things -
1) Compare my player aggregate rating to a player with "all 12s" - in an attempt to recreate something like "Wins Above Replacement" as used in baseball. Basically - is my player better than the average flotsam I can pick up easily - and how much better? This helps in roster construction and transfer window opportunities, and esp if I'm playing a small or poor club (or the MLS).
2) Every time the game posts "Top 50 Best in World" to my inbox - I go and find the highest rated player in that top 50 and put them in the same ratings as I use for my players. Then I create a comparison metric that answers the question "how far away is my player from the best on the market?"
This is useful for clubs that are rich and / or when I want to dethrone PSG.
3) I'm playing with the idea of comparing my player's ability vs. how much I'm spending. I haven't come up with a good approach other than to divide the aggregate rating by salary here, which can be helpful, but I feel like there might be a better approach.
Finally, I'm playing with the idea of using/stressing the attribute testing on this forum:
https://fm-arena.com/table/13-attribute-testing/#options
Which I *love* because it shows me based on results vs. the weights. I feel like measuring based on how the simulators algorithms respond is more meaningful than just how it constructs the inputs into the simulator.
Why you don't use FMRTE? I think its more advanced scouting tool and also with much more flexibility on ratings etc. I was using Genie Scout too before some years but the developer of the app don’t update it anymore with new features, only fixing some bugs and make it work for each edition/patch.
Thank you again for your time and your effort you put. Keep going
A couple of reasons:
First, I play on an iPad. So it’s easier for me to just use an XLS (which I really only use 3-4 times a season.
Second, I have custom metrics that help me evaluate a player. So if I decide to switch to PC or Mac, if FMRTE allows me to do custom analytics - that might be an attractive option.
Third, when I play with the numbers myself - I learn more about the game maybe?
Jumping Reach
Balance
Concentration
Finishing
Strength
This suggests that Jumping Reach, Concentration and Strength, are key “defensive position heavy weight” is a key defensive performance attribute.
Balance and Finishing are big “offensive position heavy weight” and key offensive performance attributes.
So maybe we’re looking at 3 Tiers of interesting attributes -
Tier 1 - ykykyk down to, say, Aggression (or Decisions, if you’d like) PLUS for offense Balance and Finishing, and then for defense, Jumping Reach, Concentration, Strength.
Tier 2 - ykykyk everything else between, say, Teamwork and Tackling
Tier 3 - everything else.
Then all you’d have to do is figure out how much a 17 in, say, Determination or Flair is worth, compared to a 12 in Pace, vs. a 14 in 1st Touch…
Basically, I wanted to compare the two, so I aggregated the value of each attribute in YKYKYK (with and without GK position). Then created an average for the attribute across all positions. You'll see that in the first two columns, sorted from highest to lowest. P/A are top, leadership on the bottom.
Then took the attribute testing from this site, and ranked that from most impact to least (and made a little %). Here's the results.
First, I want to say that if you play on PC and are good with Python, you should really follow what Squirrel_Plays is doing on Youtube. It's amazing, it's the right way to go about stuff like this, total option for automation and quantitative analysis.
However, if you play on Touch/Console or just aren't that great at Python (I fit both categories) you might want a spreadsheet tool to help with player evaluations and transfer windows and such.
I made one, it's on my github here:
https://github.com/AlexanderHutton/AlexFMXLS/blob/main/AlexFM24RatingsMkIII.xlsx
It uses FMScout attribute weights for positions vs. ykykyk, but you could easily modify it to use ykykyk. (I also use a different modified version for my saves that stresses other attributes and gives different weights to attributes that I would stress for my style of play).
But here's some things you might find helpful:
1.) You enter in the player attribute numbers, and it creates a score that is the sum of all the attributes multiplied by their FMScout weights.
2.) It calculates a score that compares that aggregate player attribute score to a score of a player who has "all 12s" - trying to represent how the player might compare to an absolutely "average" player (my attempt at baseball's WAR for FManager).
3.) It also has conditional formatting that will show if the player is actually a "13.5" or above.
4.) Because nobody is perfect (no straight 20's) I also do something to compare the player to the "Best In World." Basically, what I do is wait for the game to give me the "top 50" for the year, find the best for each position, and enter their attributes for their position. The weighted aggregate of their attributes for the position are then used as a "Best In World" score and each of your team's players can be compared to the very best. So I have a Wonderkid that I've helped become "95% of an Mbappe" at his peak.
5.) I'm also experimenting with comparing the weighted scores with the player spend (to optimize roster building on poor clubs). But I'm not sold on my current approach (simply dividing the weighted aggregate by their salary).
anyway, if you like it, cool let me know. If there are flaws or improvements, let me know, too.