joshua said: Based on the coefficients for each position I calculated the relative importance of the top 3 attributes. If, like me you are lazy and want something easy to plug into the in-game scouting you can use this to get an idea of which top 2 or 3 attributes to look at for each position.
Here are the relative impacts for each position: DLR (Full Backs):
Mark said: For Importing select Data from Text, Select All file types, Select the File, Use "|" as qualifier. Should them align all the fields to columns. Hope this makes sense Expand
In Excel, in 'Data'>'Queries and Connections', change the 'Source' to the .html-file exported through the view. Expand
I managed to DL with the view you said, I go to your Excel and add the file, I import my Milan team that I copy-paste in the "Squad_Data_N" tab but in the "Squad_Derivatives_N" view everything is at 0.
Trololo said: I managed to DL with the view you said, I go to your Excel and add the file, I import my Milan team that I copy-paste in the "Squad_Data_N" tab but in the "Squad_Derivatives_N" view everything is at 0.
What am I doing wrong? Expand
To be honest, I have no clue... You could fiddle a bit with the file and see if something works out.
As for your earlier comment, when you edit the connection, you can change the 'Source' of input to the path of your .html squad export.
Trololo said: I managed to DL with the view you said, I go to your Excel and add the file, I import my Milan team that I copy-paste in the "Squad_Data_N" tab but in the "Squad_Derivatives_N" view everything is at 0.
What am I doing wrong? Expand
You're French, and the excel file is in english, so it doesn't work
DoubleR said: So it looks like pace and acceleration are still king? Expand
Yep, and Jumping Reach. Difference is that sometimes some technical attributes like Passing or Technique are showing. Previously it was mostly just Dribbling.
Orion said: Yep, and Jumping Reach. Difference is that sometimes some technical attributes like Passing or Technique are showing. Previously it was mostly just Dribbling. Expand
Thanks for the awesome work The fact you did it even for the new ME's (playing Asian Fusion myself) is really nice!
Orion said: Yep, and Jumping Reach. Difference is that sometimes some technical attributes like Passing or Technique are showing. Previously it was mostly just Dribbling. Expand
Could the current dataset be skewed by an overrepresentation of lower-level players, whose attributes may not reflect high-level gameplay? You mentioned yourself: 'the data is flooded with players with relatively low CA.' We all know that in lower leagues, and even near the bottom of top leagues, the game tends to be more primal and physical rather than technical.
By focusing on top teams and excluding young, developing players, the analysis might better capture the attributes that truly influence performance at an elite level. Given that, what do you think about refining the sample to focus on actual top-tier teams and filtering out younger, still-developing players?
I feel this would make the findings more representative of how FM is actually played: primarily in the top five leagues, with most players managing elite squads or near-peers, at least from what I’ve seen in the video content and forums. This would, in turn, make your valuable work even more useful to the community. Otherwise, many might draw the wrong conclusions from it: simply because they don’t read the full accompanying text, for whatever reason.
Hello everyone. We're back again with a new experiment regarding most important attributes for each respective position in FM24. It's based on the same methodology as in these two topics regarding Outfield players in FM23 and Goalkeepers in FM24. The difference is this time we'll get most important attributes for all positions in the game and their coefficients.
Game setup and testing environment
Game setup and testing environment is exactly the same as in the Goalkeepers in FM24 topic. Just this time we've selected only the players for each respective position.
Results
Instead of using all 3 models, due to their similarities, I decided to use only polynomial regression to get 8 key attributes for each position. The number of attributes was selected arbitrary so the number is not too high, since most of attributes will have very minor coefficients, and not to small, so we have more attributes to compare our players.
The attributes that were excluded from the model are - Corners, Free Kick Taking, Penalty Taking, Long Throws. It was decided due to those attributes affecting the outcome. Penalty takers, no matter the position, are expected to get higher ratings due to their goalscoring potential. The same applies to other set pieces that allow players to get more goals or assist, hence higher ratings, no matter their respective position.
The 'side' positions, like ML and MR were merged since the side aspect should not affect player ratings.
The results are listed below:
GK Attribute Coefficient Agi 0,014640 Ref 0,012837 Aer 0,011812 Thr 0,007465 Com 0,007436 Han 0,006255 Dec 0,005089 TRO -0,003310
STC OLD VALUES Attribute Coefficient Jum 0,024768 Pac 0,021030 Acc 0,015754 Cnt 0,014398 Dri 0,012353 Vis 0,011814 Bal 0,010338 Jum^ 0,002953
STC NEW VALUES Attribute Coefficient Jum 0,020557 Pac 0,020096 Acc 0,014496 Cnt 0,013675 Bal 0,012043 Dri 0,010739 Vis 0,009392 Cmp 0,009161
How to use it?
This attributes coefficients regards difference between players actual attributes and their league average - but since we are comparing players that will potentially play in the same league we don't have to care about it. The results will be a little bit skewed towards attributes with higher coefficients but for our purpose it's acceptable.
Take any position - for example GK. Take given attribute value and multiply it by coefficient - for example 10 Agi * 0,014640 = 0,14640. Do the same for all other attributes listed. Sum the results. This is your player 'score'. You can use your existing player as a benchmark. Look for other players in your scouting view and calculate score for every one of them. Pick a player that has the highest score.
Disclaimer
If there are 2 attributes listed, like for example 'Acc Agi' for the MLR multiply those by themselves, then square it and then use coefficient ( SQR(Acc * Agi) * Coeff). Jum^ is the power of two (so SQR(Jum * Jum) * Coeff).
Attributes names are corresponding with their English naming.
Pos_x is Positioning.
TRO is 'Rushing out (Tendency) - it's just how FM names that attribute when exporting.
Example of usage
I've use this method recently to find a replacement for the goalkeeper that wanted to leave. I've found one that had little bit higher 'score' than my current goalkeeper so in theory should be proper replacement. This is the result.
Old goalkeeper that I had to sell at the beginning of the season:
And his replacement results
Keep in mind that we had a recent promotion so previous goalkeeper rating of 6,91 was in 'weaker' environment while our current goalkeeper rating of 6,95 is in higher division.
I hope you had a good reading and will find this method useful for finding a proper replacements for your other players. [edit]
Just out of curiosity - since I have quite limited time to play the game - if you use this method please share some results if you'll find them interesting - not matter if it proves the model or negates it. Feedback about model not being right is also important for model evaluation.
[edit2]
Recalculated results for STC and MLR to use only linear coefficients to remove the issue with double attributes being used in the model.
[edit3]
NEW MATCH ENGINES UPDATE!
I've made similar simulations for the new match engines. Method was the same like for the original model. 11 season were simulated for each match engine using exactly the same setup as original project - it was actually simulating further in the game (from year 2033 to 2044). Methodology is also the same. I calculated 8 attributes for each position with their respective coefficients ranked descending. The list of engines and their respective results are listed below:
AMC Attribute Coefficient Pac 0,019289 Acc 0,018151 Ant 0,015457 Tec 0,015131 Pas 0,012255 Cnt 0,009733 Lon 0,009488 Jum 0,007665
STC Attribute Coefficient Pac 0,021277 Ant 0,013547 Jum 0,013172 Agi 0,011111 Dri 0,010546 Cro 0,009884 Cnt 0,009881 Bal 0,007499
ALL Attribute Coefficient Agi 0,015292 Acc 0,014105 Vis 0,012979 Jum 0,010721 Pas 0,009205 Dri 0,007296 Lon 0,007237 Fla 0,005854
FM Genie Scout
I've also prepared Genie Scout ratings for all 3 models (Google Drive Link). Disclaimer: - negative attributes like Rushing Out (Tendency) are not included since Genie Scout does not support negative coefficients - GK is GK, there is no Sweeper - MLR is Winger - AMLR is Fast Striker - STC is Target Striker - ALL is not included
I really like your approach, and I'll give it a try next week.
By the way, I noticed that for the DMC position in the vanilla Match Engine, only ACC has a coefficient, while Pace is 0. However, in all other MEs, these two seem to have the highest coefficients. Compared to all other positions, DMC appears to be quite an outlier in the vanilla ME. Could you maybe check again to see if there's a mistake in there?
ClaudeJ said: Could the current dataset be skewed by an overrepresentation of lower-level players, whose attributes may not reflect high-level gameplay? You mentioned yourself: 'the data is flooded with players with relatively low CA.' We all know that in lower leagues, and even near the bottom of top leagues, the game tends to be more primal and physical rather than technical.
By focusing on top teams and excluding young, developing players, the analysis might better capture the attributes that truly influence performance at an elite level. Given that, what do you think about refining the sample to focus on actual top-tier teams and filtering out younger, still-developing players?
I feel this would make the findings more representative of how FM is actually played: primarily in the top five leagues, with most players managing elite squads or near-peers, at least from what I’ve seen in the video content and forums. This would, in turn, make your valuable work even more useful to the community. Otherwise, many might draw the wrong conclusions from it: simply because they don’t read the full accompanying text, for whatever reason. Expand
This issue should be somehow compensate with data being standardised which means attributes for every player are 'relative' to their league average. The question with this method is - are attributes relation is linear - example if league average is 10 pace, and the player has 15 will the effect be the same as for the league having average pace 15 and player 20.
If we wanted to tackle this issue my guess would be - make a graph of league 'rank' and players average CA and based on the results filter out players playing in leagues with high enough 'rank' (just reminder - rank is a league place in continental ranking).
It's doable but would require additional work. Especially exporting again data for all the players for all the seasons for all the engines since I did not include CA in their export data.
lordus said: I really like your approach, and I'll give it a try next week.
By the way, I noticed that for the DMC position in the vanilla Match Engine, only ACC has a coefficient, while Pace is 0. However, in all other MEs, these two seem to have the highest coefficients. Compared to all other positions, DMC appears to be quite an outlier in the vanilla ME. Could you maybe check again to see if there's a mistake in there? Expand
This is only an anecdotal evidence but I had in my playing experience cases where DMC was this particular position that did not required great physicals from the player (maybe beside balance - but this is not that affected by ageing). I had a 36yo veteran playing DLP in DMC slot with far below league and even team average physicals while maintaining one of the best average rating - even thou he had almost no goals and assist so his rating was not 'inflated' with g/a.
I had a plan on doing another 10 years simulation for 'vanilla ME' because now in this testing save it's so far in the game that there are almost no real players, only the newgens. So the results would be not skewed towards real players attributes - and we know that for certain positions real player have different attribute distribution than newgens with Fullbacks/Wingbacks being good example since the newgens there have usually relatively low crossing for example.
Orion said: This issue should be somehow compensate with data being standardised which means attributes for every player are 'relative' to their league average. The question with this method is - are attributes relation is linear - example if league average is 10 pace, and the player has 15 will the effect be the same as for the league having average pace 15 and player 20.
If we wanted to tackle this issue my guess would be - make a graph of league 'rank' and players average CA and based on the results filter out players playing in leagues with high enough 'rank' (just reminder - rank is a league place in continental ranking).
It's doable but would require additional work. Especially exporting again data for all the players for all the seasons for all the engines since I did not include CA in their export data. Expand
My main concern is that the average includes non-playing players, possibly those not even on the squad roster. While your work has merit and could serve as a base for some sort of star rating for junior player development, I take issue with it being presented as 'the most important attributes for each respective position in FM24.' This phrasing gives a sense of absolute truth, which, in my opinion, leads to misguided conclusions.
I understand that refining the dataset would require a lot of additional work, but I truly believe it would better serve its intended purpose by providing a clearer and more accurate representation of attribute importance at a competitive level.
PS: ingame, each competition has a reputation value, regardless of its continent, and that could serve as a global relative ranking.
joshua said: Based on the coefficients for each position I calculated the relative importance of the top 3 attributes. If, like me you are lazy and want something easy to plug into the in-game scouting you can use this to get an idea of which top 2 or 3 attributes to look at for each position.
Here are the relative impacts for each position:
DLR (Full Backs):
Pace: 100%
Jumping: 76.8%
Acceleration: 66.7%
DC (Center Backs):
Jumping: 100%
Pace: 70.2%
Acceleration: 59.9%
WBLR (Wing Backs):
Pace: 100%
Acceleration: 96.8%
Jumping: 71.7%
DMC (Defensive Mid):
Acceleration: 100%
Anticipation: 63.4%
Stamina: 50.3%
MLR (Wide Mid):
Pace: 100%
Dribbling: 68.4%
Acceleration: 62.9%
MC (Central Mid):
Anticipation: 100%
Acceleration: 89.9%
Composure: 89.9%
AMLR (Wide Attack):
Pace: 100%
Acceleration: 83.7%
Anticipation: 64.6%
AMC (Attack Mid):
Pace: 100%
Acceleration: 97.5%
Concentration: 81.7%
STC (Striker):
Jumping: 100%
Pace: 97.8%
Acceleration: 70.5%
What about GKs?
Middleweight165 said: What about GKs?
You can just use
GK:
Agility: 100%
Reflex: 87,7%
Aerial Reach: 80,6%
Mark said: For Importing select Data from Text, Select All file types, Select the File, Use "|" as qualifier. Should them align all the fields to columns. Hope this makes sense
yes it works, tks !!
DeFlow said: I use a modified version of this one: https://www.reddit.com/r/footballmanagergames/comments/17hkiip/fm_squad_assessment_spreadsheet_v7_fm24_ready/
Use the 'Export_squad'-view in this link: fm_squad_assessment_spreadsheet_v7. Export your squad through the game (Ctrl-P > Export to .html).
This is the excel-sheet I made:
Homebrew Player Rating Coefficients Excel-sheet
In Excel, in 'Data'>'Queries and Connections', change the 'Source' to the .html-file exported through the view.
Hi,
in 'Data'>'Queries and Connections', how do you change the 'Source' to the .html-file ?
DeFlow said: I use a modified version of this one: https://www.reddit.com/r/footballmanagergames/comments/17hkiip/fm_squad_assessment_spreadsheet_v7_fm24_ready/
Use the 'Export_squad'-view in this link: fm_squad_assessment_spreadsheet_v7. Export your squad through the game (Ctrl-P > Export to .html).
This is the excel-sheet I made:
Homebrew Player Rating Coefficients Excel-sheet
In Excel, in 'Data'>'Queries and Connections', change the 'Source' to the .html-file exported through the view.
I managed to DL with the view you said, I go to your Excel and add the file, I import my Milan team that I copy-paste in the "Squad_Data_N" tab but in the "Squad_Derivatives_N" view everything is at 0.
What am I doing wrong?
Trololo said: I managed to DL with the view you said, I go to your Excel and add the file, I import my Milan team that I copy-paste in the "Squad_Data_N" tab but in the "Squad_Derivatives_N" view everything is at 0.
What am I doing wrong?
To be honest, I have no clue... You could fiddle a bit with the file and see if something works out.
As for your earlier comment, when you edit the connection, you can change the 'Source' of input to the path of your .html squad export.
Trololo said: I managed to DL with the view you said, I go to your Excel and add the file, I import my Milan team that I copy-paste in the "Squad_Data_N" tab but in the "Squad_Derivatives_N" view everything is at 0.
What am I doing wrong?
You're French, and the excel file is in english, so it doesn't work
Updated first post with coefficients for new match engines. Posting for visibility.
So it looks like pace and acceleration are still king?
DoubleR said: So it looks like pace and acceleration are still king?
Yep, and Jumping Reach. Difference is that sometimes some technical attributes like Passing or Technique are showing. Previously it was mostly just Dribbling.
Orion said: Yep, and Jumping Reach. Difference is that sometimes some technical attributes like Passing or Technique are showing. Previously it was mostly just Dribbling.
The fact you did it even for the new ME's (playing Asian Fusion myself) is really nice!
Thanks for the awesome work
Orion said: Yep, and Jumping Reach. Difference is that sometimes some technical attributes like Passing or Technique are showing. Previously it was mostly just Dribbling.
Could the current dataset be skewed by an overrepresentation of lower-level players, whose attributes may not reflect high-level gameplay? You mentioned yourself: 'the data is flooded with players with relatively low CA.' We all know that in lower leagues, and even near the bottom of top leagues, the game tends to be more primal and physical rather than technical.
By focusing on top teams and excluding young, developing players, the analysis might better capture the attributes that truly influence performance at an elite level. Given that, what do you think about refining the sample to focus on actual top-tier teams and filtering out younger, still-developing players?
I feel this would make the findings more representative of how FM is actually played: primarily in the top five leagues, with most players managing elite squads or near-peers, at least from what I’ve seen in the video content and forums. This would, in turn, make your valuable work even more useful to the community. Otherwise, many might draw the wrong conclusions from it: simply because they don’t read the full accompanying text, for whatever reason.
Orion said: Introduction
Hello everyone. We're back again with a new experiment regarding most important attributes for each respective position in FM24. It's based on the same methodology as in these two topics regarding Outfield players in FM23 and Goalkeepers in FM24. The difference is this time we'll get most important attributes for all positions in the game and their coefficients.
Game setup and testing environment
Game setup and testing environment is exactly the same as in the Goalkeepers in FM24 topic. Just this time we've selected only the players for each respective position.
Results
Instead of using all 3 models, due to their similarities, I decided to use only polynomial regression to get 8 key attributes for each position. The number of attributes was selected arbitrary so the number is not too high, since most of attributes will have very minor coefficients, and not to small, so we have more attributes to compare our players.
The attributes that were excluded from the model are - Corners, Free Kick Taking, Penalty Taking, Long Throws. It was decided due to those attributes affecting the outcome. Penalty takers, no matter the position, are expected to get higher ratings due to their goalscoring potential. The same applies to other set pieces that allow players to get more goals or assist, hence higher ratings, no matter their respective position.
The 'side' positions, like ML and MR were merged since the side aspect should not affect player ratings.
The results are listed below:
GK
Attribute Coefficient
Agi 0,014640
Ref 0,012837
Aer 0,011812
Thr 0,007465
Com 0,007436
Han 0,006255
Dec 0,005089
TRO -0,003310
DLR
Attribute Coefficient
Pac 0,018991
Jum 0,014582
Acc 0,012670
Ant 0,012269
Cnt 0,009911
Dri 0,008246
Cmp 0,007720
Cro 0,006812
DC
Attribute Coefficient
Jum 0,022608
Pac 0,0158820
Acc 0,013536
Wor 0,010819
Ant 0,010326
Pos_x 0,008864
Pas 0,008826
Cnt 0,008358
WBLR
Attribute Coefficient
Pac 0,019196
Acc 0,018585
Jum 0,013761
Cmp 0,011762
Vis 0,010025
Cro 0,009596
Wor 0,008166
Det 0,005121
DMC
Attribute Coefficient
Acc 0,020572
Ant 0,013047
Sta 0,010352
Jum 0,009796
Cmp 0,009470
Pas 0,009114
Lon 0,007952
Dri 0,007338
MLR OLD VALUES
Attribute Coefficient
Pac 0,022852
Acc 0,018679
Dri 0,016960
Tec 0,013665
Jum 0,010993
Vis 0,010804
Cnt 0,009576
Acc Agi 0,003329
MLR NEW VALUES
Attribute Coefficient
Pac 0,020497
Dri 0,014018
Acc 0,012883
Cmp 0,012072
Vis 0,011542
Jum 0,011150
Cro 0,010598
Sta 0,009658
MC
Attribute Coefficient
Ant 0,014011
Acc 0,012595
Cmp 0,012589
Pac 0,012156
Cro 0,010100
Dri 0,008134
Jum 0,007918
Str 0,006528
AMLR
Attribute Coefficient
Pac 0,023458
Acc 0,019640
Ant 0,015160
Cro 0,014857
Dri 0,013533
Jum 0,013029
Tec 0,012662
Cmp 0,012295
AMC
Attribute Coefficient
Pac 0,016763
Acc 0,016348
Cnt 0,013697
Cmp 0,012813
Tec 0,011647
Lon 0,009914
Jum 0,009524
Dri 0,008679
STC OLD VALUES
Attribute Coefficient
Jum 0,024768
Pac 0,021030
Acc 0,015754
Cnt 0,014398
Dri 0,012353
Vis 0,011814
Bal 0,010338
Jum^ 0,002953
STC NEW VALUES
Attribute Coefficient
Jum 0,020557
Pac 0,020096
Acc 0,014496
Cnt 0,013675
Bal 0,012043
Dri 0,010739
Vis 0,009392
Cmp 0,009161
How to use it?
This attributes coefficients regards difference between players actual attributes and their league average - but since we are comparing players that will potentially play in the same league we don't have to care about it. The results will be a little bit skewed towards attributes with higher coefficients but for our purpose it's acceptable.
Take any position - for example GK. Take given attribute value and multiply it by coefficient - for example 10 Agi * 0,014640 = 0,14640. Do the same for all other attributes listed. Sum the results. This is your player 'score'. You can use your existing player as a benchmark. Look for other players in your scouting view and calculate score for every one of them. Pick a player that has the highest score.
Disclaimer
If there are 2 attributes listed, like for example 'Acc Agi' for the MLR multiply those by themselves, then square it and then use coefficient ( SQR(Acc * Agi) * Coeff). Jum^ is the power of two (so SQR(Jum * Jum) * Coeff).
Attributes names are corresponding with their English naming.
Pos_x is Positioning.
TRO is 'Rushing out (Tendency) - it's just how FM names that attribute when exporting.
Example of usage
I've use this method recently to find a replacement for the goalkeeper that wanted to leave. I've found one that had little bit higher 'score' than my current goalkeeper so in theory should be proper replacement. This is the result.
Old goalkeeper that I had to sell at the beginning of the season:
And his replacement results
Keep in mind that we had a recent promotion so previous goalkeeper rating of 6,91 was in 'weaker' environment while our current goalkeeper rating of 6,95 is in higher division.
I hope you had a good reading and will find this method useful for finding a proper replacements for your other players.
[edit]
Just out of curiosity - since I have quite limited time to play the game - if you use this method please share some results if you'll find them interesting - not matter if it proves the model or negates it. Feedback about model not being right is also important for model evaluation.
[edit2]
Recalculated results for STC and MLR to use only linear coefficients to remove the issue with double attributes being used in the model.
[edit3]
NEW MATCH ENGINES UPDATE!
I've made similar simulations for the new match engines.
Method was the same like for the original model. 11 season were simulated for each match engine using exactly the same setup as original project - it was actually simulating further in the game (from year 2033 to 2044).
Methodology is also the same. I calculated 8 attributes for each position with their respective coefficients ranked descending.
The list of engines and their respective results are listed below:
Asian Fusion
GK
Attribute Coefficient
Agi 0,013862
Ref 0,01223
Aer 0,011153
Dec 0,007721
Com 0,006883
Thr 0,005364
TRO -0,004394
Wor 0,003918
DLR
Attribute Coefficient
Acc 0,014419
Pac 0,01425
Jum 0,012579
Ant 0,011949
Cmp 0,010953
Cnt 0,010615
Agi 0,00894
Cro 0,006053
DC
Jum 0,020336
Acc 0,013794
Cnt 0,012809
Ant 0,012786
Pac 0,011036
Agi 0,007869
Dri 0,007604
Vis 0,007175
WBLR
Pac 0,020999
Acc 0,01613
Ant 0,014263
Cmp 0,01227
Jum 0,011309
Cro 0,009785
Nat 0,00598
Bal 0,005673
DMC
Attribute Coefficient
Pac 0,015115
Acc 0,014695
Ant 0,009336
Jum 0,008994
Cnt 0,008862
Pas 0,008854
Dri 0,008205
OtB 0,007905
MLR
Attribute Coefficient
Pac 0,020733
Acc 0,017484
Ant 0,014751
Dri 0,011262
Vis 0,010851
Cro 0,009974
OtB 0,009782
Jum 0,009303
MC
Attribute Coefficient
Pac 0,015818
Acc 0,013322
Dri 0,010707
Cmp 0,010609
Cnt 0,009394
Ant 0,009217
Lon 0,008809
Jum 0,006312
AMLR
Attribute Coefficient
Pac 0,023192
Acc 0,020691
Ant 0,015043
Dri 0,013602
Cro 0,012183
Vis 0,012012
Tec 0,011933
Jum 0,011169
AMC
Attribute Coefficient
Pac 0,0202
Acc 0,014382
Cnt 0,011833
Ant 0,011825
Fla 0,011125
Dri 0,010094
Lon 0,010083
Jum 0,007762
STC
Attribute Coefficient
Pac 0,021063
Ant 0,015483
Acc 0,015151
Jum 0,014779
Fla 0,013387
Cnt 0,012599
Dri 0,01092
Cro 0,009355
ALL (model including outfield players from all positions)
Attribute Coefficient
Ant 0,014785
Pac 0,013272
Acc 0,01191
Jum 0,011831
Agi 0,009973
Lon 0,007401
Cmp 0,006749
Dri 0,006559
FMTweak
GK
Attribute Coefficient
Agi 0,012505
Aer 0,011164
Ref 0,010801
Com 0,006723
Han 0,00641
Thr 0,006367
TRO -0,006245
Pac 0,004643
DLR
Attribute Coefficient
Acc 0,013597
Jum 0,01306
Pac 0,012236
Ant 0,011565
Agi 0,009327
Fir 0,008223
Cmp 0,007745
Cnt 0,007133
DC
Attribute Coefficient
Jum 0,01907
Acc 0,015935
Agi 0,012246
Pos_x 0,010717
Cnt 0,01056
Ant 0,008858
Vis 0,008293
Fla 0,007868
WBLR
Attribute Coefficient
Pac 0,017325
Ant 0,012306
Jum 0,011456
Cmp 0,011074
Agi 0,01054
Tec 0,009274
Acc 0,008873
Dri 0,008574
DMC
Attribute Coefficient
Pac 0,014999
Acc 0,013865
OtB 0,012342
Ant 0,010224
Jum 0,00853
Pas 0,00806
Vis 0,007703
Wor 0,007163
MLR
Attribute Coefficient
Pac 0,021969
Acc 0,016877
Ant 0,016714
OtB 0,015771
Pas 0,013718
Dri 0,010168
Cro 0,008961
Jum 0,008427
MC
Attribute Coefficient
Pac 0,01696
Dri 0,01232
Acc 0,010894
Cmp 0,010411
Ant 0,010086
Vis 0,009814
Cro 0,009064
Jum 0,006533
AMLR
Attribute Coefficient
Pac 0,025619
Acc 0,017786
Vis 0,015335
Tec 0,014416
Dri 0,014384
Cro 0,010459
Jum 0,009989
Cnt 0,009832
AMC
Attribute Coefficient
Pac 0,019289
Acc 0,018151
Ant 0,015457
Tec 0,015131
Pas 0,012255
Cnt 0,009733
Lon 0,009488
Jum 0,007665
STC
Attribute Coefficient
Pac 0,021277
Ant 0,013547
Jum 0,013172
Agi 0,011111
Dri 0,010546
Cro 0,009884
Cnt 0,009881
Bal 0,007499
ALL
Attribute Coefficient
Agi 0,015292
Acc 0,014105
Vis 0,012979
Jum 0,010721
Pas 0,009205
Dri 0,007296
Lon 0,007237
Fla 0,005854
FM Genie Scout
I've also prepared Genie Scout ratings for all 3 models (Google Drive Link).
Disclaimer:
- negative attributes like Rushing Out (Tendency) are not included since Genie Scout does not support negative coefficients
- GK is GK, there is no Sweeper
- MLR is Winger
- AMLR is Fast Striker
- STC is Target Striker
- ALL is not included
Excel sheet with all the coefficients and calculated Weights
I really like your approach, and I'll give it a try next week.
By the way, I noticed that for the DMC position in the vanilla Match Engine, only ACC has a coefficient, while Pace is 0. However, in all other MEs, these two seem to have the highest coefficients. Compared to all other positions, DMC appears to be quite an outlier in the vanilla ME.
Could you maybe check again to see if there's a mistake in there?
ClaudeJ said: Could the current dataset be skewed by an overrepresentation of lower-level players, whose attributes may not reflect high-level gameplay? You mentioned yourself: 'the data is flooded with players with relatively low CA.' We all know that in lower leagues, and even near the bottom of top leagues, the game tends to be more primal and physical rather than technical.
By focusing on top teams and excluding young, developing players, the analysis might better capture the attributes that truly influence performance at an elite level. Given that, what do you think about refining the sample to focus on actual top-tier teams and filtering out younger, still-developing players?
I feel this would make the findings more representative of how FM is actually played: primarily in the top five leagues, with most players managing elite squads or near-peers, at least from what I’ve seen in the video content and forums. This would, in turn, make your valuable work even more useful to the community. Otherwise, many might draw the wrong conclusions from it: simply because they don’t read the full accompanying text, for whatever reason.
This issue should be somehow compensate with data being standardised which means attributes for every player are 'relative' to their league average. The question with this method is - are attributes relation is linear - example if league average is 10 pace, and the player has 15 will the effect be the same as for the league having average pace 15 and player 20.
If we wanted to tackle this issue my guess would be - make a graph of league 'rank' and players average CA and based on the results filter out players playing in leagues with high enough 'rank' (just reminder - rank is a league place in continental ranking).
It's doable but would require additional work. Especially exporting again data for all the players for all the seasons for all the engines since I did not include CA in their export data.
lordus said: I really like your approach, and I'll give it a try next week.
By the way, I noticed that for the DMC position in the vanilla Match Engine, only ACC has a coefficient, while Pace is 0. However, in all other MEs, these two seem to have the highest coefficients. Compared to all other positions, DMC appears to be quite an outlier in the vanilla ME.
Could you maybe check again to see if there's a mistake in there?
This is only an anecdotal evidence but I had in my playing experience cases where DMC was this particular position that did not required great physicals from the player (maybe beside balance - but this is not that affected by ageing). I had a 36yo veteran playing DLP in DMC slot with far below league and even team average physicals while maintaining one of the best average rating - even thou he had almost no goals and assist so his rating was not 'inflated' with g/a.
I had a plan on doing another 10 years simulation for 'vanilla ME' because now in this testing save it's so far in the game that there are almost no real players, only the newgens. So the results would be not skewed towards real players attributes - and we know that for certain positions real player have different attribute distribution than newgens with Fullbacks/Wingbacks being good example since the newgens there have usually relatively low crossing for example.
Orion said: This issue should be somehow compensate with data being standardised which means attributes for every player are 'relative' to their league average. The question with this method is - are attributes relation is linear - example if league average is 10 pace, and the player has 15 will the effect be the same as for the league having average pace 15 and player 20.
If we wanted to tackle this issue my guess would be - make a graph of league 'rank' and players average CA and based on the results filter out players playing in leagues with high enough 'rank' (just reminder - rank is a league place in continental ranking).
It's doable but would require additional work. Especially exporting again data for all the players for all the seasons for all the engines since I did not include CA in their export data.
My main concern is that the average includes non-playing players, possibly those not even on the squad roster. While your work has merit and could serve as a base for some sort of star rating for junior player development, I take issue with it being presented as 'the most important attributes for each respective position in FM24.' This phrasing gives a sense of absolute truth, which, in my opinion, leads to misguided conclusions.
I understand that refining the dataset would require a lot of additional work, but I truly believe it would better serve its intended purpose by providing a clearer and more accurate representation of attribute importance at a competitive level.
PS: ingame, each competition has a reputation value, regardless of its continent, and that could serve as a global relative ranking.
Cheers