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.
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.